OpenRaiser/Intern-Atlas huggingface dataset:
3D Surface Reconstruction of Underwater Objects
Venue: arXiv cs.CV 2012
Year: 2012

In this paper, we propose a novel technique to reconstruct 3D surface of an underwater object using stereo images. Reconstructing the 3D surface of an underwater object is really a challenging task due to degraded quality of underwater images. There are various reason of quality degradation of underwater images i.e., non-uniform illumination of light on the surface of objects, scattering and absorption effects. Floating particles present in underwater produces Gaussian noise on the captured underwater images which degrades the quality of images. The degraded underwater images are preprocessed by applying homomorphic, wavelet denoising and anisotropic filtering sequentially. The uncalibrated rectification technique is applied to preprocessed images to rectify the left and right images. The rectified left and right image lies on a common plane. To find the correspondence points in a left and right images, we have applied dense stereo matching technique i.e., graph cut method. Finally, we estimate the depth of images using triangulation technique. The experimental result shows that the proposed method reconstruct 3D surface of underwater objects accurately using captured underwater stereo images.
Trees in simple Polygons
Venue: arXiv cs.CG 2012
Year: 2012

We prove that every simple polygon contains a degree 3 tree encompassing a prescribed set of vertices. We give tight bounds on the minimal number of degree 3 vertices. We apply this result to reprove a result from Bose et al. that every set of disjoint line segments in the plane admits a binary tree.
Dynamic Decision Support System Based on Bayesian Networks Application to fight against the Nosocomial Infections
Venue: arXiv cs.AI 2012
Year: 2012

The improvement of medical care quality is a significant interest for the future years. The fight against nosocomial infections (NI) in the intensive care units (ICU) is a good example. We will focus on a set of observations which reflect the dynamic aspect of the decision, result of the application of a Medical Decision Support System (MDSS). This system has to make dynamic decision on temporal data. We use dynamic Bayesian network (DBN) to model this dynamic process. It is a temporal reasoning within a real-time environment; we are interested in the Dynamic Decision Support Systems in healthcare domain (MDDSS).
NF-SAVO: Neuro-Fuzzy system for Arabic Video OCR
Venue: arXiv cs.CV 2012
Year: 2012

In this paper we propose a robust approach for text extraction and recognition from video clips which is called Neuro-Fuzzy system for Arabic Video OCR. In Arabic video text recognition, a number of noise components provide the text relatively more complicated to separate from the background. Further, the characters can be moving or presented in a diversity of colors, sizes and fonts that are not uniform. Added to this, is the fact that the background is usually moving making text extraction a more intricate process. Video include two kinds of text, scene text and artificial text. Scene text is usually text that becomes part of the scene itself as it is recorded at the time of filming the scene. But artificial text is produced separately and away from the scene and is laid over it at a later stage or during the post processing time. The emergence of artificial text is consequently vigilantly directed. This type of text carries with it important information that helps in video referencing, indexing and retrieval.
A Novel Anticlustering Filtering Algorithm for the Prediction of Genes as a Drug Target
Venue: arXiv cs.CE 2012
Year: 2012

The high-throughput data generated by microarray experiments provides complete set of genes being expressed in a given cell or in an organism under particular conditions. The analysis of these enormous data has opened a new dimension for the researchers. In this paper we describe a novel algorithm to microarray data analysis focusing on the identification of genes that are differentially expressed in particular internal or external conditions and which could be potential drug targets. The algorithm uses the time-series gene expression data as an input and recognizes genes which are expressed differentially. This algorithm implements standard statistics-based gene functional investigations, such as the log transformation, mean, log-sigmoid function, coefficient of variations, etc. It does not use clustering analysis. The proposed algorithm has been implemented in Perl. The time-series gene expression data on yeast Saccharomyces cerevisiae from the Stanford Microarray Database (SMD)consisting of 6154 genes have been taken for the validation of the algorithm. The developed method extracted 48 genes out of total 6154 genes. These genes are mostly responsible for the yeast's resistants at a high temperature.
Dating Texts without Explicit Temporal Cues
Venue: arXiv cs.CL 2012
Year: 2012

This paper tackles temporal resolution of documents, such as determining when a document is about or when it was written, based only on its text. We apply techniques from information retrieval that predict dates via language models over a discretized timeline. Unlike most previous works, we rely {\it solely} on temporal cues implicit in the text. We consider both document-likelihood and divergence based techniques and several smoothing methods for both of them. Our best model predicts the mid-point of individuals' lives with a median of 22 and mean error of 36 years for Wikipedia biographies from 3800 B.C. to the present day. We also show that this approach works well when training on such biographies and predicting dates both for non-biographical Wikipedia pages about specific years (500 B.C. to 2010 A.D.) and for publication dates of short stories (1798 to 2008). Together, our work shows that, even in absence of temporal extraction resources, it is possible to achieve remarkable temporal locality across a diverse set of texts.
Probabilistic Combination of Classifier and Cluster Ensembles for Non-transductive Learning
Venue: arXiv cs.LG 2012
Year: 2012

Unsupervised models can provide supplementary soft constraints to help classify new target data under the assumption that similar objects in the target set are more likely to share the same class label. Such models can also help detect possible differences between training and target distributions, which is useful in applications where concept drift may take place. This paper describes a Bayesian framework that takes as input class labels from existing classifiers (designed based on labeled data from the source domain), as well as cluster labels from a cluster ensemble operating solely on the target data to be classified, and yields a consensus labeling of the target data. This framework is particularly useful when the statistics of the target data drift or change from those of the training data. We also show that the proposed framework is privacy-aware and allows performing distributed learning when data/models have sharing restrictions. Experiments show that our framework can yield superior results to those provided by applying classifier ensembles only.
Privacy Preserving Web Query Log Publishing: A Survey on Anonymization Techniques
Venue: arXiv cs.DB 2012
Year: 2012

Releasing Web query logs which contain valuable information for research or marketing, can breach the privacy of search engine users. Therefore rendering query logs to limit linking a query to an individual while preserving the data usefulness for analysis, is an important research problem. This survey provides an overview and discussion on the recent studies on this direction.
A Conceptual Framework for the Promotion of Trusted Online Retailing Environment in Saudi Arabia
Venue: arXiv cs.CY 2012
Year: 2012

This paper presents a model conceptual framework that is aimed at promoting trust in the online retailing environment in the Kingdom of Saudi Arabia (KSA). Despite rapid Internet growth, the development of online retailing in Saudi Arabia continues to progress very slowly compared to that of the developed and leading developing countries. To determine the reason behind the sluggish growth of online retailing in the KSA, a mixed methods study involving retailers and customers was conducted in four stages. The outcomes of the study point to distrust in the online retailing environment in Saudi Arabia as a key inhibitory factor for growth. As such, a five-part model is proposed to promote trust in the online shopping environment in the KSA.
Factors influencing the decision of Saudi consumers to purchase form online retailers: Quantitative Analysis
Venue: arXiv cs.CY 2012
Year: 2012

This paper presents the preliminary findings of a study researching the diffusion and the adoption of online retailing in Saudi Arabia. It reports new research that identifies and explores the key issues that positively and negatively influence the decision of Saudi customers to buy from online retailers in Saudi Arabia. Although Saudi Arabia has the largest and fastest growth of ICT marketplaces in the Arab region, e-commerce activities are not progressing at the same speed. While the overall research project involves exploratory research using mixed methods, the focus of this paper is on a quantitative analysis of responses obtained from a survey of Saudi customers, with the design of the questionnaire instrument being based on the findings of a qualitative analysis reported in a previous paper. The main findings of the current analysis include a list of key factors that affect Saudi customers' purchase from Saudi online retailers, and quantitative indications of the relative strengths of the various relationships.
On the Existence of Telescopers for Mixed Hypergeometric Terms
Venue: arXiv cs.SC 2012
Year: 2012

We present a criterion for the existence of telescopers for mixed hypergeometric terms, which is based on multiplicative and additive decompositions. The criterion enables us to determine the termination of Zeilberger's algorithms for mixed hypergeometric inputs.
Power Control and Interference Management in Dense Wireless Networks
Venue: arXiv cs.IT 2012
Year: 2012

We address the problem of interference management and power control in terms of maximization of a general utility function. For the utility functions under consideration, we propose a power control algorithm based on a fixed-point iteration; further, we prove local convergence of the algorithm in the neighborhood of the optimal power vector. Our algorithm has several benefits over the previously studied works in the literature: first, the algorithm can be applied to problems other than network utility maximization (NUM), e.g., power control in a relay network; second, for a network with $N$ wireless transmitters, the computational complexity of the proposed algorithm is $\mathcal{O}(N^2)$ calculations per iteration (significantly smaller than the $\mathcal{O}(N^3) $ calculations for Newton's iterations or gradient descent approaches). Furthermore, the algorithm converges very fast (usually in less than 15 iterations), and in particular, if initialized close to the optimal solution, the convergence speed is much faster. This suggests the potential of tracking variations in slowly fading channels. Finally, when implemented in a distributed fashion, the algorithm attains the optimal power vector with a signaling/computational complexity of only $\mathcal{O}(N)$ at each node.
Route Planning Made Easy - An Automated System for Sri Lanka
Venue: arXiv cs.CY 2012
Year: 2012

Commercial cities like Colombo constantly face to problem of traffic congestion due to the large number of people visiting the city for various reasons. Also these cities have a large number of roads with many roads connecting any two selected locations. Finding the best path between two locations in Colombo city is not a trivial task, due to the complexity of the road network and other reasons such as heavy traffic, changes to the road networks such as road closures and one-ways. This paper presents the results of a study carried out to understand this problem and development of a system to plan the travel way ahead of the planned day or time of the journey. This system can compute the best route from between two locations taking multiple factors such as traffic conditions, road closures or one-way declarations etc., into account. This system also has the capability to compute the best route between any two locations on a future date based on the road conditions on that date. The system comprises three main modules and two user interfaces one for normal users and the other for administrators. The Administrative interface can only be accessed via web browser running on a computer, while the other interface can be accessed either via a web browser or a GPRS enabled mobile phone. The system is powered mainly by the Geographic Information System (GIS) technology and the other supporting technologies used are database management system, ASP.Net technology and the GPRS technology. Finally the developed system was evaluated for its functionality and user friendliness using a user survey. The results of the survey are also presented in this paper.
FlowME: Lattice-based Traffic Measurement
Venue: arXiv cs.NI 2012
Year: 2012

Flow-based traffic measurement is a very challenging problem: Managing counters for each individual traffic flow in hardware resources knowingly struggle to scale with high-speed links. In this paper we propose a novel lattice theory-based approach that improves flow-based measurement performances and scales by keeping the number of the maintained hardware counters to a minimum (result mathematically established in the paper). The crucial contribution of the lattice is to map the computational semantics of the packet processing to user requests for traffic measurement thus allowing for a better-informed and focused counter assignment. An implementation over an Openflow switch, FlowME, was developed and evaluated upon its memory usage, performance overhead, and processing effort to generate the minimal solution. Experimental results indicate a significant decrease in resource consumption.
Analysis of AODV over increased density and mobility in Intelligent Transportation System
Venue: arXiv cs.NI 2012
Year: 2012

Currently the area of VANET lacks in having some better designed algorithms to handle dynamic change and frequent disruption due to the high mobility of the vehicles. There are many techniques to disseminate messages across the moving vehicles but they are all highly dependent on some conditions involving flow, density and speed. The two techniques that are commonly used are AODV (Ad Hoc on Demand Distance Vector) and DSRC (Dedicated Short Range Communication). This work presents a detailed analysis of AODV. This study is focused on the use of AODV in Intelligent Transportation System. The limitations in the working of AODV routing protocol has been identified and proved. These limitations can be removed to some extent in order to increase the performance of vehicular networks and make the driving more safe and easy for a normal user as well as the implementation complications will be removed and an efficient system implementation will be possible.
A semantic cache for enhancing Web services communities activities: Health care case study
Venue: arXiv cs.DL 2012
Year: 2012

Collective memories are strong support for enhancing the activities of capitalization, management and dissemination inside a Web services community.
A Type System for the Automatic Distribution of Higher-order Synchronous Dataflow Programs
Venue: arXiv cs.PL 2012
Year: 2012

We address the design of distributed systems with synchronous dataflow programming languages. As modular design entails handling both architectural and functional modularity, our first contribution is to extend an existing synchronous dataflow programming language with primitives allowing the description of a distributed architecture and the localization of some expressions onto some processors. We also present a distributed semantics to formalize the distributed execution of synchronous programs. Our second contribution is to provide a type system, in order to infer the localization of non-annotated values by means of type inference and to ensure, at compilation time, the consistency of the distribution. Our third contribution is to provide a type-directed projection operation to obtain automatically,from a centralized typed program, the local program to be executed by each computing resource. The type system as well as the automatic distribution mechanism has been fully implemented in the compiler of an existing synchronous data-flow programming language.
Using ontology for resume annotation
Venue: arXiv cs.IR 2012
Year: 2012

Employers collect a large number of resumes from job portals, or from the company's own website. These documents are used for an automated selection of candidates satisfying the requirements and therefore reducing recruitment costs. Various approaches for process documents have already been developed for recruitment. In this paper we present an approach based on semantic annotation of resumes for e-recruitment process. The most important task consists on modelling the semantic content of these documents using ontology. The ontology is built taking into account the most significant components of resumes inspired from the structure of EUROPASS CV. This ontology is thereafter used to annotate automatically the resumes.
A Novel Approach to Fully Private and Secure Auction: A Sealed Bid Knapsack Auction
Venue: arXiv cs.CR 2012
Year: 2012

In an electronic auction protocol, the main participants are the seller, a set of trusted auctioneer(s) and the set of bidders. In this paper we consider the situation where there is a seller and a set of n bidders intending to come to an agreement on the selling price of a certain good. Full private or bidder-resolved auction means that this agreement is reached without the help of trusted parties or auctioneers. Therefore, only the seller and the set of bidders are involved, the role of the auctioneers becomes obsolete in this case. property.We propose a new technique for the design of a full private sealed-bid auction protocol.
The View Update Problem Revisited
Venue: arXiv cs.DB 2012
Year: 2012

In this paper, we revisit the view update problem in a relational setting and propose a framework based on the notion of determinacy under constraints. Within such a framework, we characterise when a view mapping is invertible, establishing that this is the case precisely when each database symbol has an exact rewriting in terms of the view symbols under the given constraints, and we provide a general effective criterion to understand whether the changes introduced by a view update can be propagated to the underlying database relations in a unique and unambiguous way. Afterwards, we show how determinacy under constraints can be checked, and rewritings effectively found, in three different relevant scenarios in the absence of view constraints. First, we settle the long-standing open issue of how to solve the view update problem in a multi-relational database with views that are projections of joins of relations, and we do so in a more general setting where views are defined by arbitrary conjunctive queries and database constraints are stratified embedded dependencies. Next, we study a setting based on horizontal decompositions of a single database relation, where views are defined by selections on possibly interpreted attributes (e.g., arithmetic comparisons) in the presence of domain constraints over the database schema. Lastly, we look into another multi-relational database setting, where views are defined in an expressive "Type" Relational Algebra based on the n-ary Description Logic DLR and database constraints are inclusions of expressions in that algebra.
Optimal Sequence-Based LQG Control over TCP-like Networks Subject to Random Transmission Delays and Packet Losses
Venue: arXiv cs.SY 2012
Year: 2012

This paper addresses the problem of sequence-based controller design for Networked Control Systems (NCS), where control inputs and measurements are transmitted over TCP-like network connections that are subject to stochastic packet losses and time-varying packet delays. At every time step, the controller sends a sequence of predicted control inputs to the actuator in addition to the current control input. In this sequence-based setup, we derive an optimal solution to the Linear Quadratic Gaussian (LQG) control problem and prove that the separation principle holds. Simulations demonstrate the improved performance of this optimal controller compared to other sequence-based approaches.
The relation between Granger causality and directed information theory: a review
Venue: arXiv cs.IT 2012
Year: 2012

This report reviews the conceptual and theoretical links between Granger causality and directed information theory. We begin with a short historical tour of Granger causality, concentrating on its closeness to information theory. The definitions of Granger causality based on prediction are recalled, and the importance of the observation set is discussed. We present the definitions based on conditional independence. The notion of instantaneous coupling is included in the definitions. The concept of Granger causality graphs is discussed. We present directed information theory from the perspective of studies of causal influences between stochastic processes. Causal conditioning appears to be the cornerstone for the relation between information theory and Granger causality. In the bivariate case, the fundamental measure is the directed information, which decomposes as the sum of the transfer entropies and a term quantifying instantaneous coupling. We show the decomposition of the mutual information into the sums of the transfer entropies and the instantaneous coupling measure, a relation known for the linear Gaussian case. We study the multivariate case, showing that the useful decomposition is blurred by instantaneous coupling. The links are further developed by studying how measures based on directed information theory naturally emerge from Granger causality inference frameworks as hypothesis testing.
High-Performance Reachability Query Processing under Index Size Restrictions
Venue: arXiv cs.DB 2012
Year: 2012

In this paper, we propose a scalable and highly efficient index structure for the reachability problem over graphs. We build on the well-known node interval labeling scheme where the set of vertices reachable from a particular node is compactly encoded as a collection of node identifier ranges. We impose an explicit bound on the size of the index and flexibly assign approximate reachability ranges to nodes of the graph such that the number of index probes to answer a query is minimized. The resulting tunable index structure generates a better range labeling if the space budget is increased, thus providing a direct control over the trade off between index size and the query processing performance. By using a fast recursive querying method in conjunction with our index structure, we show that in practice, reachability queries can be answered in the order of microseconds on an off-the-shelf computer - even for the case of massive-scale real world graphs. Our claims are supported by an extensive set of experimental results using a multitude of benchmark and real-world web-scale graph datasets.
An Efficient Soft Decoder of Block Codes Based on Compact Genetic Algorithm
Venue: arXiv cs.IT 2012
Year: 2012

Soft-decision decoding is NP-hard problem of great interest to developers of communication system. We present an efficient soft-decision decoding of linear block codes based on compact genetic algorithm (cGA) and compare its performance with various other decoding algorithms including Shakeel algorithms. The proposed algorithm uses the dual code in contrast to Shakeel algorithm that uses the code itself. Hence, this new approach reduces the decoding complexity of high rates codes. The complexity and an optimized version of this new algorithm is also presented and discussed.