OpenRaiser/Intern-Atlas huggingface dataset:
Post-Nonlinear Sparse Component Analysis Using Single-Source Zones and Functional Data Clustering
Venue: arXiv cs.IT 2012
Year: 2012

In this paper, we introduce a general extension of linear sparse component analysis (SCA) approaches to postnonlinear (PNL) mixtures. In particular, and contrary to the state-of-art methods, our approaches use a weak sparsity source assumption: we look for tiny temporal zones where only one source is active. We investigate two nonlinear single-source confidence measures, using the mutual information and a local linear tangent space approximation (LTSA). For this latter measure, we derive two extensions of linear single-source measures, respectively based on correlation (LTSA-correlation) and eigenvalues (LTSA-PCA). A second novelty of our approach consists of applying functional data clustering techniques to the scattered observations in the above single-source zones, thus allowing us to accurately estimate them.We first study a classical approach using a B-spline approximation, and then two approaches which locally approximate the nonlinear functions as lines. Finally, we extend our PNL methods to more general nonlinear mixtures. Combining single-source zones and functional data clustering allows us to tackle speech signals, which has never been performed by other PNL-SCA methods. We investigate the performance of our approaches with simulated PNL mixtures of real speech signals. Both the mutual information and the LTSA-correlation measures are better-suited to detecting single-source zones than the LTSA-PCA measure. We also find local-linear-approximation-based clustering approaches to be more flexible and more accurate than the B-spline one.
Principal Component Analysis-Linear Discriminant Analysis Feature Extractor for Pattern Recognition
Venue: arXiv cs.CV 2012
Year: 2012

Robustness of embedded biometric systems is of prime importance with the emergence of fourth generation communication devices and advancement in security systems This paper presents the realization of such technologies which demands reliable and error-free biometric identity verification systems. High dimensional patterns are not permitted due to eigen-decomposition in high dimensional image space and degeneration of scattering matrices in small size sample. Generalization, dimensionality reduction and maximizing the margins are controlled by minimizing weight vectors. Results show good pattern by multimodal biometric system proposed in this paper. This paper is aimed at investigating a biometric identity system using Principal Component Analysis and Lindear Discriminant Analysis with K-Nearest Neighbor and implementing such system in real-time using SignalWAVE.
Proposed congestion control method for cloud computing environments
Venue: arXiv cs.NI 2012
Year: 2012

As cloud computing services rapidly expand their customer base, it has become important to share cloud resources, so as to provide them economically. In cloud computing services, multiple types of resources, such as processing ability, bandwidth and storage, need to be allocated simultaneously. If there is a surge of requests, a competition will arise between these requests for the use of cloud resources. This leads to the disruption of the service and it is necessary to consider a measure to avoid or relieve congestion of cloud computing environments. This paper proposes a new congestion control method for cloud computing environments which reduces the size of required resource for congested resource type instead of restricting all service requests as in the existing networks. Next, this paper proposes the user service specifications for the proposed congestion control method, and clarifies the algorithm to decide the optimal size of required resource to be reduced, based on the load offered to the system. It is demonstrated by simulation evaluations that the proposed method can handle more requests compared with the conventional methods and relieve the congestion. Then, this paper proposes to enhance the proposed method, so as to enable the fair resource allocation among users in congested situation.
Towards a Security Engineering Process Model for Electronic Business Processes
Venue: arXiv cs.CR 2012
Year: 2012

Business process management (BPM) and accompanying systems aim at enabling enterprises to become adaptive. In spite of the dependency of enterprises on secure business processes, BPM languages and techniques provide only little support for security. Several complementary approaches have been proposed for security in the domain of BPM. Nevertheless, support for a systematic procedure for the development of secure electronic business processes is still missing. In this paper, we pinpoint the need for a security engineering process model in the domain of BPM and identify key requirements for such process model.
A polynomial time algorithm for computing the HNF of a module over the integers of a number field
Venue: arXiv cs.SC 2012
Year: 2012

We present a variation of the modular algorithm for computing the Hermite Normal Form of an $\OK$-module presented by Cohen, where $\OK$ is the ring of integers of a number field K. The modular strategy was conjectured to run in polynomial time by Cohen, but so far, no such proof was available in the literature. In this paper, we provide a new method to prevent the coefficient explosion and we rigorously assess its complexity with respect to the size of the input and the invariants of the field K.
Spectra: Robust Estimation of Distribution Functions in Networks
Venue: arXiv cs.DC 2012
Year: 2012

Distributed aggregation allows the derivation of a given global aggregate property from many individual local values in nodes of an interconnected network system. Simple aggregates such as minima/maxima, counts, sums and averages have been thoroughly studied in the past and are important tools for distributed algorithms and network coordination. Nonetheless, this kind of aggregates may not be comprehensive enough to characterize biased data distributions or when in presence of outliers, making the case for richer estimates of the values on the network. This work presents Spectra, a distributed algorithm for the estimation of distribution functions over large scale networks. The estimate is available at all nodes and the technique depicts important properties, namely: robust when exposed to high levels of message loss, fast convergence speed and fine precision in the estimate. It can also dynamically cope with changes of the sampled local property, not requiring algorithm restarts, and is highly resilient to node churn. The proposed approach is experimentally evaluated and contrasted to a competing state of the art distribution aggregation technique.
Connectivity of Large Wireless Networks under A Generic Connection Model
Venue: arXiv cs.NI 2012
Year: 2012

This paper provides a necessary and sufficient condition for a random network with nodes Poissonly distributed on a unit square and a pair of nodes directly connected following a generic random connection model to be asymptotically almost surely connected. The results established in this paper expand recent results obtained for connectivity of random geometric graphs from the unit disk model and the fewer results from the log-normal model to the more generic and more practical random connection model.
Learning Fuzzy β-Certain and β-Possible rules from incomplete quantitative data by rough sets
Venue: arXiv cs.DS 2012
Year: 2012

The rough-set theory proposed by Pawlak, has been widely used in dealing with data classification problems. The original rough-set model is, however, quite sensitive to noisy data. Tzung thus proposed deals with the problem of producing a set of fuzzy certain and fuzzy possible rules from quantitative data with a predefined tolerance degree of uncertainty and misclassification. This model allowed, which combines the variable precision rough-set model and the fuzzy set theory, is thus proposed to solve this problem. This paper thus deals with the problem of producing a set of fuzzy certain and fuzzy possible rules from incomplete quantitative data with a predefined tolerance degree of uncertainty and misclassification. A new method, incomplete quantitative data for rough-set model and the fuzzy set theory, is thus proposed to solve this problem. It first transforms each quantitative value into a fuzzy set of linguistic terms using membership functions and then finding incomplete quantitative data with lower and the fuzzy upper approximations. It second calculates the fuzzy β-lower and the fuzzy β-upper approximations. The certain and possible rules are then generated based on these fuzzy approximations. These rules can then be used to classify unknown objects.
Extracting Geospatial Preferences Using Relational Neighbors
Venue: arXiv cs.IR 2012
Year: 2012

With the increasing popularity of location-based social media applications and devices that automatically tag generated content with locations, large repositories of collaborative geo-referenced data are appearing on-line. Efficiently extracting user preferences from these data to determine what information to recommend is challenging because of the sheer volume of data as well as the frequency of updates. Traditional recommender systems focus on the interplay between users and items, but ignore contextual parameters such as location. In this paper we take a geospatial approach to determine locational preferences and similarities between users. We propose to capture the geographic context of user preferences for items using a relational graph, through which we are able to derive many new and state-of-the-art recommendation algorithms, including combinations of them, requiring changes only in the definition of the edge weights. Furthermore, we discuss several solutions for cold-start scenarios. Finally, we conduct experiments using two real-world datasets and provide empirical evidence that many of the proposed algorithms outperform existing location-aware recommender algorithms.
Goppa goemetry codes via elementary methods (In Portuguese)
Venue: arXiv cs.IT 2012
Year: 2012

The central objective of this dissertation was to present the Goppa Geometry Codes via elementary methods which were introduced by J.H. van Lint ,R.Pellikaan and T. Hohold about 1998. On the first part of such dissertation are presented the fundamental concepts about bodies of rational functions of an algebraic curve in the direction as to define the Goppa Codes on a classical manner. In this study we based ourselves mainly on the book ? Algebraic Function Fields and Codes? of H. Stichtenoth. The second part is initiated with an introduction about the functions weight, degree and order which are fundamental for the study of the Goppa Codes through elementary methods of linear algebra and of semigroups and such study was based on ? Algebraic Geometry Codes ? of J.H. van Lint,R.Pellikaan and T. Hohold.
Development of knowledge Base Expert System for Natural treatment of Diabetes disease
Venue: arXiv cs.AI 2012
Year: 2012

The development of expert system for treatment of Diabetes disease by using natural methods is new information technology derived from Artificial Intelligent research using ESTA (Expert System Text Animation) System. The proposed expert system contains knowledge about various methods of natural treatment methods (Massage, Herbal/Proper Nutrition, Acupuncture, Gems) for Diabetes diseases of Human Beings. The system is developed in the ESTA (Expert System shell for Text Animation) which is Visual Prolog 7.3 Application. The knowledge for the said system will be acquired from domain experts, texts and other related sources.
On Fast Computation of Gradients for CANDECOMP/PARAFAC Algorithms
Venue: arXiv cs.NA 2012
Year: 2012

Product between mode-$n$ unfolding $\bY_{(n)}$ of an $N$-D tensor $\tY$ and Khatri-Rao products of $(N-1)$ factor matrices $\bA^{(m)}$, $m = 1,..., n-1, n+1, ..., N$ exists in algorithms for CANDECOMP/PARAFAC (CP). If $\tY$ is an error tensor of a tensor approximation, this product is the gradient of a cost function with respect to factors, and has the largest workload in most CP algorithms. In this paper, a fast method to compute this product is proposed. Experimental verification shows that the fast CP gradient can accelerate the CP_ALS algorithm 2 times and 8 times faster for factorizations of 3-D and 4-D tensors, and the speed-up ratios can be 20-30 times for higher dimensional tensors.
Vision-based Human Gender Recognition: A Survey
Venue: arXiv cs.CV 2012
Year: 2012

Gender is an important demographic attribute of people. This paper provides a survey of human gender recognition in computer vision. A review of approaches exploiting information from face and whole body (either from a still image or gait sequence) is presented. We highlight the challenges faced and survey the representative methods of these approaches. Based on the results, good performance have been achieved for datasets captured under controlled environments, but there is still much work that can be done to improve the robustness of gender recognition under real-life environments.
Stable marriage and roommate problems with individual-based stability
Venue: arXiv cs.GT 2012
Year: 2012

Research regarding the stable marriage and roommate problem has a long and distinguished history in mathematics, computer science and economics. Stability in this context is predominantly core stability or one of its variants in which each deviation is by a group of players. There has been little focus in matching theory on stability concepts such as Nash stability and individual stability in which the deviation is by a single player. Such stability concepts are suitable especially when trust for the other party is limited, complex coordination is not feasible, or when only unmatched agents can be approached. Furthermore, weaker stability notions such as individual stability may in principle circumvent the negative existence and computational complexity results in matching theory. We characterize the computational complexity of checking the existence and computing individual-based stable matchings for the marriage and roommate settings. One of our key computational results for the stable marriage setting also carries over to different classes of hedonic games for which individual-based stability has already been of much interest.
New approach using Bayesian Network to improve content based image classification systems
Venue: arXiv cs.CV 2012
Year: 2012

This paper proposes a new approach based on augmented naive Bayes for image classification. Initially, each image is cutting in a whole of blocks. For each block, we compute a vector of descriptors. Then, we propose to carry out a classification of the vectors of descriptors to build a vector of labels for each image. Finally, we propose three variants of Bayesian Networks such as Naive Bayesian Network (NB), Tree Augmented Naive Bayes (TAN) and Forest Augmented Naive Bayes (FAN) to classify the image using the vector of labels. The results showed a marked improvement over the FAN, NB and TAN.
Multi-Level Coding Efficiency with Improved Quality for Image Compression based on AMBTC
Venue: arXiv cs.CV 2012
Year: 2012

In this paper, we have proposed an extended version of Absolute Moment Block Truncation Coding (AMBTC) to compress images. Generally the elements of a bitplane used in the variants of Block Truncation Coding (BTC) are of size 1 bit. But it has been extended to two bits in the proposed method. Number of statistical moments preserved to reconstruct the compressed has also been raised from 2 to 4. Hence, the quality of the reconstructed images has been improved significantly from 33.62 to 38.12 with the increase in bpp by 1. The increased bpp (3) is further reduced to 1.75in multiple levels: in one level, by dropping 4 elements of the bitplane in such a away that the pixel values of the dropped elements can easily be interpolated with out much of loss in the quality, in level two, eight elements are dropped and reconstructed later and in level three, the size of the statistical moments is reduced. The experiments were carried over standard images of varying intensities. In all the cases, the proposed method outperforms the existing AMBTC technique in terms of both PSNR and bpp.
Hiding Sensitive Association Rules without Altering the Support of Sensitive Item(s)
Venue: arXiv cs.DB 2012
Year: 2012

Association rule mining is an important data-mining technique that finds interesting association among a large set of data items. Since it may disclose patterns and various kinds of sensitive knowledge that are difficult to find otherwise, it may pose a threat to the privacy of discovered confidential information. Such information is to be protected against unauthorized access. Many strategies had been proposed to hide the information. Some use distributed databases over several sites, data perturbation, clustering, and data distortion techniques. Hiding sensitive rules problem, and still not sufficiently investigated, is the requirement to balance the confidentiality of the disclosed data with the legitimate needs of the user. The proposed approach uses the data distortion technique where the position of the sensitive items is altered but its support is never changed. The size of the database remains the same. It uses the idea of representative rules to prune the rules first and then hides the sensitive rules. Advantage of this approach is that it hides maximum number of rules however, the existing approaches fail to hide all the desired rules, which are supposed to be hidden in minimum number of passes. The paper also compares of the proposed approach with existing ones.
Computational complexity and memory usage for multi-frontal direct solvers in structured mesh finite elements
Venue: arXiv cs.NA 2012
Year: 2012

The multi-frontal direct solver is the state-of-the-art algorithm for the direct solution of sparse linear systems. This paper provides computational complexity and memory usage estimates for the application of the multi-frontal direct solver algorithm on linear systems resulting from B-spline-based isogeometric finite elements, where the mesh is a structured grid. Specifically we provide the estimates for systems resulting from $C^{p-1}$ polynomial B-spline spaces and compare them to those obtained using $C^0$ spaces.
Compensation of compliance errors in parallel manipulators composed of non-perfect kinematic chains
Venue: arXiv cs.RO 2012
Year: 2012

The paper is devoted to the compliance errors compensation for parallel manipulators under external loading. Proposed approach is based on the non-linear stiffness modeling and reduces to a proper adjusting of a target trajectory. In contrast to previous works, in addition to compliance errors caused by machining forces, the problem of assembling errors caused by inaccuracy in the kinematic chains is considered. The advantages and practical significance of the proposed approach are illustrated by examples that deal with groove milling with Orthoglide manipulator.
Object Oriented Modelling of Idea using GA based efficient key generation for e-governance security (OOMIG)
Venue: arXiv cs.CR 2012
Year: 2012

Nowadays different state and central government in India as well as abroad are taking initiative to deliver different kind of services electronically specially using Information and Communication Technology (ICT). Intruders or hackers can steal or modify the information communicated between Government and consumer through internet. To implement privacy and confidentiality of the information we must use suitable encryption technique. In this paper an Object Oriented Modelling of International Data Encryption Algorithm (IDEA) using GA based efficient key generation technique has been proposed to incorporate privacy and confidentiality of information which would be communicated between government and consumer.
Skin-color based videos categorization
Venue: arXiv cs.CV 2012
Year: 2012

On dedicated websites, people can upload videos and share it with the rest of the world. Currently these videos are cat- egorized manually by the help of the user community. In this paper, we propose a combination of color spaces with the Bayesian network approach for robust detection of skin color followed by an automated video categorization. Exper- imental results show that our method can achieve satisfactory performance for categorizing videos based on skin color.
Accounting for the Uncertainty in the Evaluation of Percentile Ranks
Venue: arXiv cs.DL 2012
Year: 2012

In a recent paper entitled "Inconsistencies of Recently Proposed Citation Impact Indicators and how to Avoid Them," Schreiber (2012, at arXiv:1202.3861) proposed (i) a method to assess tied ranks consistently and (ii) fractional attribution to percentile ranks in the case of relatively small samples (e.g., for n < 100). Schreiber's solution to the problem of how to handle tied ranks is convincing, in my opinion (cf. Pudovkin & Garfield, 2009). The fractional attribution, however, is computationally intensive and cannot be done manually for even moderately large batches of documents. Schreiber attributed scores fractionally to the six percentile rank classes used in the Science and Engineering Indicators of the U.S. National Science Board, and thus missed, in my opinion, the point that fractional attribution at the level of hundred percentiles-or equivalently quantiles as the continuous random variable-is only a linear, and therefore much less complex problem. Given the quantile-values, the non-linear attribution to the six classes or any other evaluation scheme is then a question of aggregation. A new routine based on these principles (including Schreiber's solution for tied ranks) is made available as software for the assessment of documents retrieved from the Web of Science (at http://www.leydesdorff.net/software/i3).
Adaptive Techniques to find Optimal Planar Boxes
Venue: arXiv cs.CG 2012
Year: 2012

Given a set $P$ of $n$ planar points, two axes and a real-valued score function $f()$ on subsets of $P$, the Optimal Planar Box problem consists in finding a box (i.e. axis-aligned rectangle) $H$ maximizing $f(H\cap P)$. We consider the case where $f()$ is monotone decomposable, i.e. there exists a composition function $g()$ monotone in its two arguments such that $f(A)=g(f(A_1),f(A_2))$ for every subset $A\subseteq P$ and every partition $\{A_1,A_2\}$ of $A$. In this context we propose a solution for the Optimal Planar Box problem which performs in the worst case $O(n^2\lg n)$ score compositions and coordinate comparisons, and much less on other classes of instances defined by various measures of difficulty. A side result of its own interest is a fully dynamic \textit{MCS Splay tree} data structure supporting insertions and deletions with the \emph{dynamic finger} property, improving upon previous results [Cortés et al., J.Alg. 2009].
Finding vertex-surjective graph homomorphisms
Venue: arXiv cs.DM 2012
Year: 2012

The Surjective Homomorphism problem is to test whether a given graph G called the guest graph allows a vertex-surjective homomorphism to some other given graph H called the host graph. The bijective and injective homomorphism problems can be formulated in terms of spanning subgraphs and subgraphs, and as such their computational complexity has been extensively studied. What about the surjective variant? Because this problem is NP-complete in general, we restrict the guest and the host graph to belong to graph classes G and H, respectively. We determine to what extent a certain choice of G and H influences its computational complexity. We observe that the problem is polynomial-time solvable if H is the class of paths, whereas it is NP-complete if G is the class of paths. Moreover, we show that the problem is even NP-complete on many other elementary graph classes, namely linear forests, unions of complete graphs, cographs, proper interval graphs, split graphs and trees of pathwidth at most 2. In contrast, we prove that the problem is fixed-parameter tractable in k if G is the class of trees and H is the class of trees with at most k leaves, or if G and H are equal to the class of graphs with vertex cover number at most k.