OpenRaiser/Intern-Atlas huggingface dataset:
A Multi-Agents Architecture to Learn Vision Operators and their Parameters
Venue: arXiv cs.CV 2012
Year: 2012

In a vision system, every task needs that the operators to apply should be « well chosen » and their parameters should be also « well adjusted ». The diversity of operators and the multitude of their parameters constitute a big challenge for users. As it is very difficult to make the « right » choice, lack of a specific rule, many disadvantages appear and affect the computation time and especially the quality of results. In this paper we present a multi-agent architecture to learn the best operators to apply and their best parameters for a class of images. Our architecture consists of three types of agents: User Agent, Operator Agent and Parameter Agent. The User Agent determines the phases of treatment, a library of operators and the possible values of their parameters. The Operator Agent constructs all possible combinations of operators and the Parameter Agent, the core of the architecture, adjusts the parameters of each combination by treating a large number of images. Through the reinforcement learning mechanism, our architecture does not consider only the system opportunities but also the user preferences.
Energy Efficient MAC Protocols
Venue: arXiv cs.NI 2012
Year: 2012

This paper presents a survey of energy efficiency of Medium Access Control (MAC) protocols for Wireless Body Area Sensor Networks (WBASNs). We highlight the features of MAC protocols along with their advantages and limitations in context of WBASNs. Comparison of Low Power Listening (LPL), Scheduled Contention and Time Division Multiple Access (TDMA) is also elaborated. MAC protocols with respect to different approaches and techniques which are used for energy minimization, traffic control mechanisms for collision avoidance are discussed.We also present a survey of path loss models for In-body, On-body and Off-body communications in WBASNs and analytically discuss that path loss is maximum in In-body communication because of low energy levels to take care of tissues and organs located inside the body. Survey of Power model for WBANs of CSMA/CA and beacon mode is also presented.
Cyclic Codes from Cyclotomic Sequences of Order Four
Venue: arXiv cs.IT 2012
Year: 2012

Cyclic codes are an interesting subclass of linear codes and have been used in consumer electronics, data transmission technologies, broadcast systems, and computer applications due to their efficient encoding and decoding algorithms. In this paper, three cyclotomic sequences of order four are employed to construct a number of classes of cyclic codes over $\gf(q)$ with prime length. Under certain conditions lower bounds on the minimum weight are developed. Some of the codes obtained are optimal or almost optimal. In general, the cyclic codes constructed in this paper are very good. Some of the cyclic codes obtained in this paper are closely related to almost difference sets and difference sets. As a byproduct, the $p$-rank of these (almost) difference sets are computed.
Load Balancing with Reduced Unnecessary Handoff in Energy Efficient Macro/Femto-cell based BWA Networks
Venue: arXiv cs.NI 2012
Year: 2012

The hierarchical macro/femto cell based BWA networks are observed to be quite promising for mobile operators as it improves their network coverage and capacity at the outskirt of the macro cell. However, this new technology introduces increased number of macro/femto handoff and wastage of electrical energy which in turn may affect the system performance. Users moving with high velocity or undergoing real-time transmission suffers degraded performance due to huge number of unnecessary macro/femto handoff. On the other hand, huge amount of electrical energy is wasted when a femto BS is active in the network but remains unutilized due to low network load. Our proposed energy efficient handoff decision algorithm eliminates the unnecessary handoff while balancing the load of the macro and femto cells at minimal energy consumption. The performance of the proposed algorithm is analyzed using Continuous Time Markov Chain (CTMC) Model. In addition, we have also contributed a method to determine the balanced threshold level of the received signal strength (RSS) from macro base station (BS). The balanced threshold level provides equal load distribution of the mobile users to the macro and femto BSs. The balanced threshold level is evaluated based on the distant location of the femto cells for small scaled networks. Numerical analysis shows that threshold level above the balanced threshold results in higher load distribution of the mobile users to the femto BSs.
Conceptual Modelling and The Quality of Ontologies: Endurantism Vs. Perdurantism
Venue: arXiv cs.AI 2012
Year: 2012

Ontologies are key enablers for sharing precise and machine-understandable semantics among different applications and parties. Yet, for ontologies to meet these expectations, their quality must be of a good standard. The quality of an ontology is strongly based on the design method employed. This paper addresses the design problems related to the modelling of ontologies, with specific concentration on the issues related to the quality of the conceptualisations produced. The paper aims to demonstrate the impact of the modelling paradigm adopted on the quality of ontological models and, consequently, the potential impact that such a decision can have in relation to the development of software applications. To this aim, an ontology that is conceptualised based on the Object-Role Modelling (ORM) approach (a representative of endurantism) is re-engineered into a one modelled on the basis of the Object Paradigm (OP) (a representative of perdurantism). Next, the two ontologies are analytically compared using the specified criteria. The conducted comparison highlights that using the OP for ontology conceptualisation can provide more expressive, reusable, objective and temporal ontologies than those conceptualised on the basis of the ORM approach.
Nugget Discovery with a Multi-objective Cultural Algorithm
Venue: arXiv cs.NE 2012
Year: 2012

Partial classification popularly known as nugget discovery comes under descriptive knowledge discovery. It involves mining rules for a target class of interest. Classification "If-Then" rules are the most sought out by decision makers since they are the most comprehensible form of knowledge mined by data mining techniques. The rules have certain properties namely the rule metrics which are used to evaluate them. Mining rules with user specified properties can be considered as a multi-objective optimization problem since the rules have to satisfy more than one property to be used by the user. Cultural algorithm (CA) with its knowledge sources have been used in solving many optimization problems. However research gap exists in using cultural algorithm for multi-objective optimization of rules. In the current study a multi-objective cultural algorithm is proposed for partial classification. Results of experiments on benchmark data sets reveal good performance.
On Optimal Top-K String Retrieval
Venue: arXiv cs.DS 2012
Year: 2012

Let ${\cal{D}}$ = $\{d_1, d_2, d_3, ..., d_D\}$ be a given set of $D$ (string) documents of total length $n$. The top-$k$ document retrieval problem is to index $\cal{D}$ such that when a pattern $P$ of length $p$, and a parameter $k$ come as a query, the index returns the $k$ most relevant documents to the pattern $P$. Hon et. al. \cite{HSV09} gave the first linear space framework to solve this problem in $O(p + k\log k)$ time. This was improved by Navarro and Nekrich \cite{NN12} to $O(p + k)$. These results are powerful enough to support arbitrary relevance functions like frequency, proximity, PageRank, etc. In many applications like desktop or email search, the data resides on disk and hence disk-bound indexes are needed. Despite of continued progress on this problem in terms of theoretical, practical and compression aspects, any non-trivial bounds in external memory model have so far been elusive. Internal memory (or RAM) solution to this problem decomposes the problem into $O(p)$ subproblems and thus incurs the additive factor of $O(p)$. In external memory, these approaches will lead to $O(p)$ I/Os instead of optimal $O(p/B)$ I/O term where $B$ is the block-size. We re-interpret the problem independent of $p$, as interval stabbing with priority over tree-shaped structure. This leads us to a linear space index in external memory supporting top-$k$ queries (with unsorted outputs) in near optimal $O(p/B + \log_B n + \log^{(h)} n + k/B)$ I/Os for any constant $h${$\log^{(1)}n =\log n$ and $\log^{(h)} n = \log (\log^{(h-1)} n)$}. Then we get $O(n\log^*n)$ space index with optimal $O(p/B+\log_B n + k/B)$ I/Os.
Robust Image Watermarking Under Pixel Wise Masking Framework
Venue: arXiv cs.CR 2012
Year: 2012

The current paper presents a robust watermarking method for still images, which uses the similarity of discrete wavelet transform and human visual system (HVS). The proposed scheme makes the use of pixel wise masking in order to make binary watermark imperceptible to the HVS. The watermark is embedded in the perceptually significant, spatially selected detail coefficients using sub band adaptive threshold scheme. The threshold is computed based on the statistical analysis of the wavelet coefficients. The watermark is embedded several times to achieve better robustness. Here, a new type of non-oblivious detection method is proposed. The improvement in robustness performance against different types of deliberate and non-intentional image impairments (lossy compression, scaling, cropping, filtering etc) is supported through experimental results. The reported result also shows improvement in visual and statistical invisibility of the hidden data. The proposed method is compared with a state of the art frequency based watermarking technique, highlighting its performance. This algorithmic architecture utilizes the existing allocated bandwidth in the data transmission channel in a more efficient manner.
Efficient Resource Allocation in Resource provisioning policies over Resource Cloud Communication Paradigm
Venue: arXiv cs.DC 2012
Year: 2012

Optimal resource utilization for executing tasks within the cloud is one of the biggest challenges. In executing the task over a cloud, the resource provisioner is responsible for providing the resources to create virtual machines. To utilize the resources optimally, the resource provisioner has to take care of the process of allocating resources to Virtual Machine Manager (VMM). In this paper, an efficient way to utilize the resources, within the cloud, to create virtual machines has been proposed considering optimum cost based on performance factor. This performance factor depends upon the overall cost of the resource, communication channel cost, reliability and popularity factor. We have proposed a framework for communication between resource owner and cloud using Resource Cloud Communication Paradigm (RCCP). We extend the CloudSim[2] adding provisioner policies and Efficient Resource Allocation (ERA) algorithm in VMM allocation policy as a decision support for resource provisioner.
Secure Cloud Communication for Effective Cost Management System through MSBE
Venue: arXiv cs.DC 2012
Year: 2012

In Cloud Computing Architecture, Brokers are responsible to provide services to the end users. An Effective Cost Management System (ECMS) which works over Secure Cloud Communication Paradigm (SCCP) helps in finding a communication link with overall minimum cost of links. We propose an improved Broker Cloud Communication Paradigm (BCCP) with integration of security issues. Two algorithms are included, first is Secure Optimized Route Cost Finder (S-ORCF) to find optimum route between broker and cloud on the behalf of cost factor and second is Secure Optimized Route Management (S-ORM) to maintain optimum route. These algorithms proposed with cryptographic integrity of the secure route discovery process in efficient routing approaches between broker and cloud. There is lack in Dynamic Source Routing Approach to verify whether any intermediate node has been deleted, inserted or modified with no valid authentication. We use symmetric cryptographic primitives, which is made possible due to multisource broadcast encryption scheme. This paper outlines the use of secure route discovery protocol (SRDP)that employs such a security paradigm in cloud computing.
A model driven method for promoting reuse in SOA-solutions by managing variability
Venue: arXiv cs.SE 2012
Year: 2012

Service Oriented Architecture (SOA) is an architectural paradigm that describes how organizations, people and systems provide and use services to achieve their goals and enhance productivity. Moreover, with the evolution of SOA, the focus in software development has shifted from applications to reusable services. However, the reuse in SOA is more seen as composition of fine-grained services rather than reuse of services implementation to build new services with additional functionalities. This can have some performance repercussions. Hence, in this paper, we propose a model driven method for managing Web service's variability based on MDA (Model Driven Architecture) as a way to promote reuse. In fact, through MDA, the method enables the automation of Web service's realization regardless of the supported platforms. Moreover, we present a WSDL extension meta-model called VarWSDL which enhances Web services by variability notions.
Practical Power Allocation and Greedy Partner Selection for Cooperative Networks
Venue: arXiv cs.SY 2012
Year: 2012

In this paper, we present a novel algorithm for power allocation in the Amplify-and-Forward cooperative communication that minimizes the outage probability with a given value of total power. We present the problem with new formulation and solve the optimal power allocation for a fixed set of partners. The proposed solution provides a direct power allocation scheme with a simple formula that can be also be represented by a simple lookup table which makes it easy for practical implementation. We present simulation results to demonstrate that the performances of the proposed algorithms are very close to results of the previously published iterative optimal power allocation algorithms. We also consider the issue of partner selection in a cooperative network.
Privacy Preserving MFI Based Similarity Measure For Hierarchical Document Clustering
Venue: arXiv cs.DB 2012
Year: 2012

The increasing nature of World Wide Web has imposed great challenges for researchers in improving the search efficiency over the internet. Now days web document clustering has become an important research topic to provide most relevant documents in huge volumes of results returned in response to a simple query. In this paper, first we proposed a novel approach, to precisely define clusters based on maximal frequent item set (MFI) by Apriori algorithm. Afterwards utilizing the same maximal frequent item set (MFI) based similarity measure for Hierarchical document clustering. By considering maximal frequent item sets, the dimensionality of document set is decreased. Secondly, providing privacy preserving of open web documents is to avoiding duplicate documents. There by we can protect the privacy of individual copy rights of documents. This can be achieved using equivalence relation.
Distributed Strongly Convex Optimization
Venue: arXiv cs.DC 2012
Year: 2012

A lot of effort has been invested into characterizing the convergence rates of gradient based algorithms for non-linear convex optimization. Recently, motivated by large datasets and problems in machine learning, the interest has shifted towards distributed optimization. In this work we present a distributed algorithm for strongly convex constrained optimization. Each node in a network of n computers converges to the optimum of a strongly convex, L-Lipchitz continuous, separable objective at a rate O(log (sqrt(n) T) / T) where T is the number of iterations. This rate is achieved in the online setting where the data is revealed one at a time to the nodes, and in the batch setting where each node has access to its full local dataset from the start. The same convergence rate is achieved in expectation when the subgradients used at each node are corrupted with additive zero-mean noise.
Multiple Criteria Clustering of Mobile Agents in WSN
Venue: arXiv cs.NI 2012
Year: 2012

In Wireless sensor networks data aggregation with hundreds and thousands of sensor nodes is very complex task. Recently, mobile agents have been proposed for efficient data dissemination in sensor networks. In the traditional client/server based computing architecture, data is collected from multiple sources and forwarded to destination for further processing. It requires high bandwidth, whereas in the mobile agent is a task specific executable code traverses to the relevant source for gathering data. It reduces communication overhead, reduce cost, low bandwidth. Agents have capability to perform task for multiple applications. It will send only useful information to destination node. The problem is to group similar mobile agents into a number of clusters such that each cluster has similarity in responding to a group of nodes. By clustering intelligent mobile agents, it is possible to reduce the cost of time for each individual agent, decrease the demand imposed on network for a set of required tasks, decrease total number of visits. This paper, we present the problem of Multiple Criteria Clustering of Mobile Agents (MCCMA) where the decision is to cluster mobile agents such that a group of similar intelligent mobile agents will visit a group of similar sensor nodes.
Biogeography-Based Informative Gene Selection and Cancer Classification Using SVM and Random Forests
Venue: arXiv cs.NE 2012
Year: 2012

Microarray cancer gene expression data comprise of very high dimensions. Reducing the dimensions helps in improving the overall analysis and classification performance. We propose two hybrid techniques, Biogeography - based Optimization - Random Forests (BBO - RF) and BBO - SVM (Support Vector Machines) with gene ranking as a heuristic, for microarray gene expression analysis. This heuristic is obtained from information gain filter ranking procedure. The BBO algorithm generates a population of candidate subset of genes, as part of an ecosystem of habitats, and employs the migration and mutation processes across multiple generations of the population to improve the classification accuracy. The fitness of each gene subset is assessed by the classifiers - SVM and Random Forests. The performances of these hybrid techniques are evaluated on three cancer gene expression datasets retrieved from the Kent Ridge Biomedical datasets collection and the libSVM data repository. Our results demonstrate that genes selected by the proposed techniques yield classification accuracies comparable to previously reported algorithms.
The Han-Kobayashi Region for a Class of Gaussian Interference Channels with Mixed Interference
Venue: arXiv cs.IT 2012
Year: 2012

A simple encoding scheme based on Sato's non-naïve frequency division is proposed for a class of Gaussian interference channels with mixed interference. The achievable region is shown to be equivalent to that of Costa's noiseberg region for the onesided Gaussian interference channel. This allows for an indirect proof that this simple achievable rate region is indeed equivalent to the Han-Kobayashi (HK) region with Gaussian input and with time sharing for this class of Gaussian interference channels with mixed interference.
Incremental Learning of 3D-DCT Compact Representations for Robust Visual Tracking
Venue: arXiv cs.CV 2012
Year: 2012

Visual tracking usually requires an object appearance model that is robust to changing illumination, pose and other factors encountered in video. In this paper, we construct an appearance model using the 3D discrete cosine transform (3D-DCT). The 3D-DCT is based on a set of cosine basis functions, which are determined by the dimensions of the 3D signal and thus independent of the input video data. In addition, the 3D-DCT can generate a compact energy spectrum whose high-frequency coefficients are sparse if the appearance samples are similar. By discarding these high-frequency coefficients, we simultaneously obtain a compact 3D-DCT based object representation and a signal reconstruction-based similarity measure (reflecting the information loss from signal reconstruction). To efficiently update the object representation, we propose an incremental 3D-DCT algorithm, which decomposes the 3D-DCT into successive operations of the 2D discrete cosine transform (2D-DCT) and 1D discrete cosine transform (1D-DCT) on the input video data.
Improved brain pattern recovery through ranking approaches
Venue: arXiv cs.LG 2012
Year: 2012

Inferring the functional specificity of brain regions from functional Magnetic Resonance Images (fMRI) data is a challenging statistical problem. While the General Linear Model (GLM) remains the standard approach for brain mapping, supervised learning techniques (a.k.a.} decoding) have proven to be useful to capture multivariate statistical effects distributed across voxels and brain regions. Up to now, much effort has been made to improve decoding by incorporating prior knowledge in the form of a particular regularization term. In this paper we demonstrate that further improvement can be made by accounting for non-linearities using a ranking approach rather than the commonly used least-square regression. Through simulation, we compare the recovery properties of our approach to linear models commonly used in fMRI based decoding. We demonstrate the superiority of ranking with a real fMRI dataset.
Diagnosing client faults using SVM-based intelligent inference from TCP packet traces
Venue: arXiv cs.NI 2012
Year: 2012

We present the Intelligent Automated Client Diagnostic (IACD) system, which only relies on inference from Transmission Control Protocol (TCP) packet traces for rapid diagnosis of client device problems that cause network performance issues. Using soft-margin Support Vector Machine (SVM) classifiers, the system (i) distinguishes link problems from client problems, and (ii) identifies characteristics unique to client faults to report the root cause of the client device problem. Experimental evaluation demonstrated the capability of the IACD system to distinguish between faulty and healthy links and to diagnose the client faults with 98% accuracy in healthy links. The system can perform fault diagnosis independent of the client's specific TCP implementation, enabling diagnosis capability on diverse range of client computers.
Directed Acyclic Subgraph Problem Parameterized above the Poljak-Turzik Bound
Venue: arXiv cs.DS 2012
Year: 2012

An oriented graph is a directed graph without directed 2-cycles. Poljak and Turzík (1986) proved that every connected oriented graph $G$ on $n$ vertices and $m$ arcs contains an acyclic subgraph with at least $\frac{m}{2}+\frac{n-1}{4}$ arcs. Raman and Saurabh (2006) gave another proof of this result and left it as an open question to establish the parameterized complexity of the following problem: does $G$ have an acyclic subgraph with least $\frac{m}{2}+\frac{n-1}{4}+k$ arcs, where $k$ is the parameter? We answer this question by showing that the problem can be solved by an algorithm of runtime $(12k)!n^{O(1)}$. Thus, the problem is fixed-parameter tractable. We also prove that there is a polynomial time algorithm that either establishes that the input instance of the problem is a Yes-instance or reduces the input instance to an equivalent one of size $O(k^2)$.