Hashing Image Patches for Zooming

Abstract: In this paper we present a Bayesian image zooming/super-resolution algorithm based on a patch based representation. We work on a patch based model with overlap and employ a Locally Linear Embedding (LLE) based approach as our data fidelity term in the Bayesian inference. The image prior imposes continuity constraints across the overlapping patches. We apply an error back-projection technique, with an approximate cross bilateral filter. The problem of nearest neighbor search is handled by a variant of the locality sensitive hashing (LSH) scheme. The novelty of our work lies in the speed up achieved by the hashing scheme and the robustness and inherent modularity and parallel structure achieved by the LLE setup. The ill-posedness of the image reconstruction problem is handled by the introduction of regularization priors which encode the knowledge present in vast collections of natural images. We present comparative results for both run-time as well as visual image quality based measurements.


An Oracle Strongly Separating Deterministic Time from Nondeterministic Time, via Kolmogorov Complexity

Abstract: Hartmanis used Kolmogorov complexity to provide an alternate proof of the classical result of Baker, Gill, and Solovay that there is an oracle relative to which P is not NP. We refine the technique to strengthen the result, constructing an oracle relative to which a conjecture of Lipton is false.


Finite Optimal Control for Time-Bounded Reachability in CTMDPs and Continuous-Time Markov Games

Abstract: We establish the existence of optimal scheduling strategies for time-bounded reachability in continuous-time Markov decision processes, and of co-optimal strategies for continuous-time Markov games. Furthermore, we show that optimal control does not only exist, but has a surprisingly simple structure: The optimal schedulers from our proofs are deterministic and timed-positional, and the bounded time can be divided into a finite number of intervals, in which the optimal strategies are positional. That is, we demonstrate the existence of finite optimal control. Finally, we show that these pleasant properties of Markov decision processes extend to the more general class of continuous-time Markov games, and that both early and late schedulers show this behaviour.


Database Security: A Historical Perspective

Abstract: The importance of security in database research has greatly increased over the years as most of critical functionality of the business and military enterprises became digitized. Database is an integral part of any information system and they often hold sensitive data. The security of the data depends on physical security, OS security and DBMS security. Database security can be compromised by obtaining sensitive data, changing data or degrading availability of the database. Over the last 30 years the information technology environment have gone through many changes of evolution and the database research community have tried to stay a step ahead of the upcoming threats to the database security. The database research community has thoughts about these issues long before they were address by the implementations. This paper will examine the different topics pertaining to database security and see the adaption of the research to the changing environment. Some short term database research trends will be ascertained at the conclusion.


n-Level Graph Partitioning

Abstract: We present a multi-level graph partitioning algorithm based on the extreme idea to contract only a single edge on each level of the hierarchy. This obviates the need for a matching algorithm and promises very good partitioning quality since there are very few changes between two levels. Using an efficient data structure and new flexible ways to break local search improvements early, we obtain an algorithm that scales to large inputs and produces the best known partitioning results for many inputs. For example, in Walshaw's well known benchmark tables we achieve 155 improvements dominating the entries for large graphs.


Computers and the Conservation of Energy

Abstract: The purpose of this report is to show that computer and allied technologies can be used to increase energy efficiency. The report is divided into transport, industrial, commercial and domestic sections, which correspond to the major energy consuming sectors of the economy. Each section considers the various ways in which energy can be saved by the use of the computer. The report concludes that it is economic to incorporate computer based energy management systems in a wide variety of applications and that it is important that this capability is realised on a large scale. A comprehensive reference list and a bibliography are included.


Efficient volume sampling for row/column subset selection

Abstract: We give efficient algorithms for volume sampling, i.e., for picking $k$-subsets of the rows of any given matrix with probabilities proportional to the squared volumes of the simplices defined by them and the origin (or the squared volumes of the parallelepipeds defined by these subsets of rows). This solves an open problem from the monograph on spectral algorithms by Kannan and Vempala. Our first algorithm for volume sampling $k$-subsets of rows from an $m$-by-$n$ matrix runs in $O(kmn^{\omega} \log n)$ arithmetic operations and a second variant of it for $(1+\epsilon)$-approximate volume sampling runs in $O(mn \log m \cdot k^{2}/\epsilon^{2} + m \log^{\omega} m \cdot k^{2\omega+1}/\epsilon^{2\omega} \cdot \log(k \epsilon^{-1} \log m))$ arithmetic operations, which is almost linear in the size of the input (i.e., the number of entries) for small $k$. Our efficient volume sampling algorithms imply several interesting results for low-rank matrix approximation.


A General Framework for Graph Sparsification

Abstract: Given a weighted graph $G$ and an error parameter $\epsilon > 0$, the {\em graph sparsification} problem requires sampling edges in $G$ and giving the sampled edges appropriate weights to obtain a sparse graph $G_{\epsilon}$ (containing O(n\log n) edges in expectation) with the following property: the weight of every cut in $G_{\epsilon}$ is within a factor of $(1\pm \epsilon)$ of the weight of the corresponding cut in $G$. We provide a generic framework that sets out sufficient conditions for any particular sampling scheme to result in good sparsifiers, and obtain a set of results by simple instantiations of this framework. The results we obtain include the following: (1) We improve the time complexity of graph sparsification from O(m\log^3 n) to O(m + n\log^4 n) for graphs with polynomial edge weights. (2) We improve the time complexity of graph sparsification from O(m\log^3 n) to O(m\log^2 n) for graphs with arbitrary edge weights. (3) If the size of the sparsifier is allowed to be O(n\log^2 n/\epsilon^2) instead of O(n\log n/\epsilon^2), we improve the time complexity of sparsification to O(m) for graphs with polynomial edge weights. (4) We show that sampling using standard connectivities results in good sparsifiers, thus resolving an open question of Benczur and Karger. As a corollary, we give a simple proof of (a slightly weaker version of) a result due to Spielman and Srivastava showing that sampling using effective resistances produces good sparsifiers. (5) We give a simple proof showing that sampling using strong connectivities results in good sparsifiers, a result obtained previously using a more involved proof by Benczur and Karger. A key ingredient of our proofs is a generalization of bounds on the number of small cuts in an undirected graph due to Karger; this generalization might be of independent interest.


Real-Time Alert Correlation with Type Graphs

Abstract: The premise of automated alert correlation is to accept that false alerts from a low level intrusion detection system are inevitable and use attack models to explain the output in an understandable way. Several algorithms exist for this purpose which use attack graphs to model the ways in which attacks can be combined. These algorithms can be classified in to two broad categories namely scenario-graph approaches, which create an attack model starting from a vulnerability assessment and type-graph approaches which rely on an abstract model of the relations between attack types. Some research in to improving the efficiency of type-graph correlation has been carried out but this research has ignored the hypothesizing of missing alerts. Our work is to present a novel type-graph algorithm which unifies correlation and hypothesizing in to a single operation. Our experimental results indicate that the approach is extremely efficient in the face of intensive alerts and produces compact output graphs comparable to other techniques.


STORM - A Novel Information Fusion and Cluster Interpretation Technique

Abstract: Analysis of data without labels is commonly subject to scrutiny by unsupervised machine learning techniques. Such techniques provide more meaningful representations, useful for better understanding of a problem at hand, than by looking only at the data itself. Although abundant expert knowledge exists in many areas where unlabelled data is examined, such knowledge is rarely incorporated into automatic analysis. Incorporation of expert knowledge is frequently a matter of combining multiple data sources from disparate hypothetical spaces. In cases where such spaces belong to different data types, this task becomes even more challenging. In this paper we present a novel immune-inspired method that enables the fusion of such disparate types of data for a specific set of problems. We show that our method provides a better visual understanding of one hypothetical space with the help of data from another hypothetical space. We believe that our model has implications for the field of exploratory data analysis and knowledge discovery.


Operator-oriented programming: a new paradigm for implementing window interfaces and parallel algorithms

Abstract: We present a new programming paradigm which can be useful, in particular, for implementing window interfaces and parallel algorithms. This paradigm allows a user to define operators which can contain nested operators. The new paradigm is called operator-oriented. One of the goals of this paradigm is to escape the complexity of objects definitions inherent in many object-oriented languages and to move to transparent algorithms definitions.


An approximate analytical (structural) superposition in terms of two, or more, "alfa"-circuits of the same topology: Pt.1 - description of the superposition

Abstract: One-ports named "f-circuits", composed of similar conductors described by a monotonic polynomial, or quasi-polynomial (i.e. with positive but not necessarily integer, powers) characteristic i = f(v) are studied, focusing on the algebraic map f --> F. Here F(.) is the input conductivity characteristic; i.e., iin = F(vin) is the input current. The "power-law" "alfa-circuit" introduced in [1], for which f(v) ~ v^"alfa", is an important particular case. By means of a generalization of a parallel connection, the f-circuits are constructed from the alfa-circuits of the same topology, with different "alfa", so that the given topology is kept, and 'f' is an additive function of the connection. We observe and consider an associated, generally approximated, but, in all of the cases studied, always high-precision, specific superposition. This superposition is in terms of f --> F, and it means that F(.) of the connection is close to the sum of the input currents of the independent "alfa"-circuits, all connected in parallel to the same source. In other words, F(.) is well approximated by a linear combination of the same degrees of the independent variable as in f(.), i.e. the map of the characteristics f --> F is close to a linear one. This unexpected result is useful for understanding nonlinear algebraic circuits, and is missed in the classical theory. The cases of f(v) = D1v + D2v^2 and f(v) = D1v + D3v^3, are analyzed in examples. Special topologies when the superposition must be ideal, are also considered. In the second part [2] of the work the "circuit mechanism" that is responsible for the high precision of the superposition, in the most general case, will be explained.


Simulation of Wireless Sensor Networks Using TinyOS- A case Study

Abstract: This paper has been withdrawn by the author due to a crucial sign error in equation 1


Displacement Calculus

Abstract: The Lambek calculus provides a foundation for categorial grammar in the form of a logic of concatenation. But natural language is characterized by dependencies which may also be discontinuous. In this paper we introduce the displacement calculus, a generalization of Lambek calculus, which preserves its good proof-theoretic properties while embracing discontinuiity and subsuming it. We illustrate linguistic applications and prove Cut-elimination, the subformula property, and decidability


Graph Triangulations and the Compatibility of Unrooted Phylogenetic Trees

Abstract: We characterize the compatibility of a collection of unrooted phylogenetic trees as a question of determining whether a graph derived from these trees --- the display graph --- has a specific kind of triangulation, which we call legal. Our result is a counterpart to the well known triangulation-based characterization of the compatibility of undirected multi-state characters.


Symmetric M-tree

Abstract: The M-tree is a paged, dynamically balanced metric access method that responds gracefully to the insertion of new objects. To date, no algorithm has been published for the corresponding Delete operation. We believe this to be non-trivial because of the design of the M-tree's Insert algorithm. We propose a modification to Insert that overcomes this problem and give the corresponding Delete algorithm. The performance of the tree is comparable to the M-tree and offers additional benefits in terms of supported operations, which we briefly discuss.


Settling the Polynomial Learnability of Mixtures of Gaussians

Abstract: Given data drawn from a mixture of multivariate Gaussians, a basic problem is to accurately estimate the mixture parameters. We give an algorithm for this problem that has a running time, and data requirement polynomial in the dimension and the inverse of the desired accuracy, with provably minimal assumptions on the Gaussians. As simple consequences of our learning algorithm, we can perform near-optimal clustering of the sample points and density estimation for mixtures of k Gaussians, efficiently. The building blocks of our algorithm are based on the work Kalai et al. [STOC 2010] that gives an efficient algorithm for learning mixtures of two Gaussians by considering a series of projections down to one dimension, and applying the method of moments to each univariate projection. A major technical hurdle in Kalai et al. is showing that one can efficiently learn univariate mixtures of two Gaussians. In contrast, because pathological scenarios can arise when considering univariate projections of mixtures of more than two Gaussians, the bulk of the work in this paper concerns how to leverage an algorithm for learning univariate mixtures (of many Gaussians) to yield an efficient algorithm for learning in high dimensions. Our algorithm employs hierarchical clustering and rescaling, together with delicate methods for backtracking and recovering from failures that can occur in our univariate algorithm. Finally, while the running time and data requirements of our algorithm depend exponentially on the number of Gaussians in the mixture, we prove that such a dependence is necessary.


Average case performance of heuristics for multi-dimensional assignment problems

Abstract: We consider multi-dimensional assignment problems in a probabilistic setting. Our main results are: (i) A new efficient algorithm for the 3-dimensional planar problem, based on enumerating and selecting from a set of "alternating-path trees"; (ii) A new efficient matching-based algorithm for the 3-dimensional axial problem.


A Sparse Johnson--Lindenstrauss Transform

Abstract: Dimension reduction is a key algorithmic tool with many applications including nearest-neighbor search, compressed sensing and linear algebra in the streaming model. In this work we obtain a {\em sparse} version of the fundamental tool in dimension reduction --- the Johnson--Lindenstrauss transform. Using hashing and local densification, we construct a sparse projection matrix with just $\tilde{O}(\frac{1}{\epsilon})$ non-zero entries per column. We also show a matching lower bound on the sparsity for a large class of projection matrices. Our bounds are somewhat surprising, given the known lower bounds of $\Omega(\frac{1}{\epsilon^2})$ both on the number of rows of any projection matrix and on the sparsity of projection matrices generated by natural constructions. Using this, we achieve an $\tilde{O}(\frac{1}{\epsilon})$ update time per non-zero element for a $(1\pm\epsilon)$-approximate projection, thereby substantially outperforming the $\tilde{O}(\frac{1}{\epsilon^2})$ update time required by prior approaches. A variant of our method offers the same guarantees for sparse vectors, yet its $\tilde{O}(d)$ worst case running time matches the best approach of Ailon and Liberty.


Error Concealment in Image Communication Using Edge Map Watermarking and Spatial Smoothing

Abstract: We propose a novel error concealment algorithm to be used at the receiver side of a lossy image transmission system. Our algorithm involves hiding the edge map of the original image at the transmitter within itself using a robust watermarking scheme. At the receiver, wherever a lost block is detected, the extracted edge information is used as border constraint for the spatial smoothing employing the intact neighboring blocks in order to conceal errors. Simulation results show the superiority of our technique over existing methods even in case of high packet loss ratios in the communication network.


Intelligent Technologies in Model Base Management System Design Automation

Abstract: The article describes the prospects of model base management system design automation for decision support systems and suggests the toolbox scheme for design automation based on intelligent technologies.


Space-efficient scheduling of stochastically generated tasks

Abstract: We study the problem of scheduling tasks for execution by a processor when the tasks can stochastically generate new tasks. Tasks can be of different types, and each type has a fixed, known probability of generating other tasks. We present results on the random variable S^sigma modeling the maximal space needed by the processor to store the currently active tasks when acting under the scheduler sigma. We obtain tail bounds for the distribution of S^sigma for both offline and online schedulers, and investigate the expected value of S^sigma.


Analysis of Graphs for Digital Preservation Suitability

Abstract: We investigate the use of autonomically created small-world graphs as a framework for the long term storage of digital objects on the Web in a potentially hostile environment. We attack the classic Erdos - Renyi random, Barab'asi and Albert power law, Watts - Strogatz small world and our Unsupervised Small-World (USW) graphs using different attacker strategies and report their respective robustness. Using different attacker profiles, we construct a game where the attacker is allowed to use a strategy of his choice to remove a percentage of each graph's elements. The graph is then allowed to repair some portion of its self. We report on the number of alternating attack and repair turns until either the graph is disconnected, or the game exceeds the number of permitted turns. Based on our analysis, an attack strategy that focuses on removing the vertices with the highest betweenness value is most advantageous to the attacker. Power law graphs can become disconnected with the removal of a single edge; random graphs with the removal of as few as 1% of their vertices, small-world graphs with the removal of 14% vertices, and USW with the removal of 17% vertices. Watts - Strogatz small-world graphs are more robust and resilient than random or power law graphs. USW graphs are more robust and resilient than small world graphs. A graph of USW connected web objects (WOs) filled with data could outlive the individuals and institutions that created the data in an environment where WOs are lost due to random failures or directed attacks.


Bounds on the Maximum Number of Concurrent Links in MIMO Ad Hoc Networks with QoS Constraints

Abstract: Multiple-Input Multiple-Output (MIMO) based Medium Access Control (MAC) protocols have received a good deal of attention as researchers look to enhance overall performance of Ad Hoc networks by leveraging multi antenna enabled nodes. To date such MAC protocols have been evaluated through comparative simulation based studies that report on the number of concurrent links the protocol can support. However, a bound on the maximum number of concurrent links (MNCL) that a MIMO based MAC protocol should strive to achieve has hitherto been unavailable. In this paper we present a theoretical formulation for calculating the bound on the MNCL in a Mobile Ad Hoc Network (MANET) where the nodes have multiple antenna capability, while guaranteeing a minimum Quality of Service (QoS). In an attempt to make our findings as practical and realistic as possible, the study incorporates models for the following PHY layer and channel dependent elements: (a) path loss and fast fading effects, in order to accurately model adjacent link interference; (b) a Minimum Mean Squared Error (MMSE) based detector in the receiver which provides a balance between completely nulling of neighboring interference and hardware complexity. In calculating the bound on the MNCL our work also delivers the optimal power control solution for the network as well as the optimal link selection. The results are readily applicable to MIMO systems using Receive Diversity, Space Time Block Coding (STBC), and Transmit Beamforming and show that with a 4 element antenna system, as much as 3x improvement in the total number of concurrent links can be achieved relative to a SISO based network. The results also show diminishing improvement as the number of antennas is increased beyond 4, and the maximum allowable transmit power is increased beyond 10 dBm (for the simulated parameters).


The cooperative game theory foundations of network bargaining games

Abstract: We study bargaining games between suppliers and manufacturers in a network context. Agents wish to enter into contracts in order to generate surplus which then must be divided among the participants. Potential contracts and their surplus are represented by weighted edges in our bipartite network. Each agent in the market is additionally limited by a capacity representing the number of contracts which he or she may undertake. When all agents are limited to just one contract each, prior research applied natural generalizations of the Nash bargaining solution to the networked setting, defined the new solution concepts of stable and balanced, and characterized the resulting bargaining outcomes. We simplify and generalize these results to a setting in which participants in only one side of the market are limited to one contract each. The heart of our results uses a linear-programming formulation to establish a novel connection between well-studied cooperative game theory concepts (such as core and prekernel) and the solution concepts of stable and balanced defined for the bargaining games. This immediately implies one can take advantage of the results and algorithms in cooperative game theory to reproduce results such as those of Azar et al. [1] and Kleinberg and Tardos [29] and also generalize them to our setting. The cooperative-game-theoretic connection also inspires us to refine our solution space using standard solution concepts from that literature such as nucleolus and lexicographic kernel. The nucleolus is particularly attractive as it is unique, always exists, and is supported by experimental data in the network bargaining literature. Guided by algorithms from cooperative game theory, we show how to compute the nucleolus by pruning and iteratively solving a natural linear-programming formulation.


PROTECT: Proximity-based Trust-advisor using Encounters for Mobile Societies

Abstract: Many interactions between network users rely on trust, which is becoming particularly important given the security breaches in the Internet today. These problems are further exacerbated by the dynamics in wireless mobile networks. In this paper we address the issue of trust advisory and establishment in mobile networks, with application to ad hoc networks, including DTNs. We utilize encounters in mobile societies in novel ways, noticing that mobility provides opportunities to build proximity, location and similarity based trust. Four new trust advisor filters are introduced - including encounter frequency, duration, behavior vectors and behavior matrices - and evaluated over an extensive set of real-world traces collected from a major university. Two sets of statistical analyses are performed; the first examines the underlying encounter relationships in mobile societies, and the second evaluates DTN routing in mobile peer-to-peer networks using trust and selfishness models. We find that for the analyzed trace, trust filters are stable in terms of growth with time (3 filters have close to 90% overlap of users over a period of 9 weeks) and the results produced by different filters are noticeably different. In our analysis for trust and selfishness model, our trust filters largely undo the effect of selfishness on the unreachability in a network. Thus improving the connectivity in a network with selfish nodes. We hope that our initial promising results open the door for further research on proximity-based trust.


Analysis of Basis Pursuit Via Capacity Sets

Abstract: Finding the sparsest solution $\alpha$ for an under-determined linear system of equations $D\alpha=s$ is of interest in many applications. This problem is known to be NP-hard. Recent work studied conditions on the support size of $\alpha$ that allow its recovery using L1-minimization, via the Basis Pursuit algorithm. These conditions are often relying on a scalar property of $D$ called the mutual-coherence. In this work we introduce an alternative set of features of an arbitrarily given $D$, called the "capacity sets". We show how those could be used to analyze the performance of the basis pursuit, leading to improved bounds and predictions of performance. Both theoretical and numerical methods are presented, all using the capacity values, and shown to lead to improved assessments of the basis pursuit success in finding the sparest solution of $D\alpha=s$.


Towards Closed World Reasoning in Dynamic Open Worlds (Extended Version)

Abstract: The need for integration of ontologies with nonmonotonic rules has been gaining importance in a number of areas, such as the Semantic Web. A number of researchers addressed this problem by proposing a unified semantics for hybrid knowledge bases composed of both an ontology (expressed in a fragment of first-order logic) and nonmonotonic rules. These semantics have matured over the years, but only provide solutions for the static case when knowledge does not need to evolve. In this paper we take a first step towards addressing the dynamics of hybrid knowledge bases. We focus on knowledge updates and, considering the state of the art of belief update, ontology update and rule update, we show that current solutions are only partial and difficult to combine. Then we extend the existing work on ABox updates with rules, provide a semantics for such evolving hybrid knowledge bases and study its basic properties. To the best of our knowledge, this is the first time that an update operator is proposed for hybrid knowledge bases.


SHIELD: Social sensing and Help In Emergency using mobiLe Devices

Abstract: School and College campuses face a perceived threat of violent crimes and require a realistic plan against unpredictable emergencies and disasters. Existing emergency systems (e.g., 911, campus-wide alerts) are quite useful, but provide delayed response (often tens of minutes) and do not utilize proximity or locality. There is a need to augment such systems with proximity-based systems for more immediate response to attempt to prevent and deter crime. In this paper we propose SHIELD, an on-campus emergency rescue and alert management service. It is a fully distributed infrastructure-less platform based on proximity-enabled trust and cooperation. It relies on localized responses, sent using Bluetooth and/or WiFi on the fly to achieve minimal response time and maximal availability thereby augmenting the traditional notion of emergency services. Analysis of campus crime statistics and WLAN traces surprisingly show a strong positive correlation (over 55%) between on-campus crime statistics and spatio-temporal density distribution of on-campus mobile users. This result provides a motivation to develop such platform and points to the promise in reducing crime incidences. We also show an implementation of a prototype application to be used in such scenarios.


Browser-Based Covert Data Exfiltration

Abstract: Current best practices heavily control user permissions on network systems. This effectively mitigates many insider threats regarding the collection and exfiltration of data. Many methods of covert communication involve crafting custom packets, typically requiring both the necessary software and elevated privileges on the system. By exploiting the functionality of a browser, covert channels for data exfiltration may be created without additional software or user privileges. This paper explores novel methods of using a browser's JavaScript engine to exfiltrate documents over the Domain Name System (DNS) protocol without sending less covert Hypertext Transfer Protocol (HTTP) requests.


Detecting DNS Tunnels Using Character Frequency Analysis

Abstract: High-bandwidth covert channels pose significant risks to sensitive and proprietary information inside company networks. Domain Name System (DNS) tunnels provide a means to covertly infiltrate and exfiltrate large amounts of information passed network boundaries. This paper explores the possibility of detecting DNS tunnels by analyzing the unigram, bigram, and trigram character frequencies of domains in DNS queries and responses. It is empirically shown how domains follow Zipf's law in a similar pattern to natural languages, whereas tunneled traffic has more evenly distributed character frequencies. This approach allows tunnels to be detected across multiple domains, whereas previous methods typically concentrate on monitoring point to point systems. Anomalies are quickly discovered when tunneled traffic is compared to the character frequency fingerprint of legitimate domain traffic.


NgViz: Detecting DNS Tunnels through N-Gram Visualization and Quantitative Analysis

Abstract: This paper introduced NgViz, a tool that examines DNS traffic and shows anomalies in n-gram frequencies. This is accomplished by comparing input files against a fingerprint of legitimate traffic. Both quantitative analysis and visual aids are provided that allow the user to make determinations about the legitimacy of the DNS traffic.


Spatially-Adaptive Reconstruction in Computed Tomography Based on Statistical Learning

Abstract: We propose a direct reconstruction algorithm for Computed Tomography, based on a local fusion of a few preliminary image estimates by means of a non-linear fusion rule. One such rule is based on a signal denoising technique which is spatially adaptive to the unknown local smoothness. Another, more powerful fusion rule, is based on a neural network trained off-line with a high-quality training set of images. Two types of linear reconstruction algorithms for the preliminary images are employed for two different reconstruction tasks. For an entire image reconstruction from full projection data, the proposed scheme uses a sequence of Filtered Back-Projection algorithms with a gradually growing cut-off frequency. To recover a Region Of Interest only from local projections, statistically-trained linear reconstruction algorithms are employed. Numerical experiments display the improvement in reconstruction quality when compared to linear reconstruction algorithms.


Self-Assembly of Arbitrary Shapes Using RNAse Enzymes: Meeting the Kolmogorov Bound with Small Scale Factor (extended abstract)

Abstract: We consider a model of algorithmic self-assembly of geometric shapes out of square Wang tiles studied in SODA 2010, in which there are two types of tiles (e.g., constructed out of DNA and RNA material) and one operation that destroys all tiles of a particular type (e.g., an RNAse enzyme destroys all RNA tiles). We show that a single use of this destruction operation enables much more efficient construction of arbitrary shapes. In particular, an arbitrary shape can be constructed using an asymptotically optimal number of distinct tile types (related to the shape's Kolmogorov complexity), after scaling the shape by only a logarithmic factor. By contrast, without the destruction operation, the best such result has a scale factor at least linear in the size of the shape, and is connected only by a spanning tree of the scaled tiles. We also characterize a large collection of shapes that can be constructed efficiently without any scaling.


Optimal Data Placement on Networks With Constant Number of Clients

Abstract: We introduce optimal algorithms for the problems of data placement (DP) and page placement (PP) in networks with a constant number of clients each of which has limited storage availability and issues requests for data objects. The objective for both problems is to efficiently utilize each client's storage (deciding where to place replicas of objects) so that the total incurred access and installation cost over all clients is minimized. In the PP problem an extra constraint on the maximum number of clients served by a single client must be satisfied. Our algorithms solve both problems optimally when all objects have uniform lengths. When objects lengths are non-uniform we also find the optimal solution, albeit a small, asymptotically tight violation of each client's storage size by $\epsilon$lmax where lmax is the maximum length of the objects and $\epsilon$ some arbitrarily small positive constant. We make no assumption on the underlying topology of the network (metric, ultrametric etc.), thus obtaining the first non-trivial results for non-metric data placement problems.


Efficient Learning with Partially Observed Attributes

Abstract: We describe and analyze efficient algorithms for learning a linear predictor from examples when the learner can only view a few attributes of each training example. This is the case, for instance, in medical research, where each patient participating in the experiment is only willing to go through a small number of tests. Our analysis bounds the number of additional examples sufficient to compensate for the lack of full information on each training example. We demonstrate the efficiency of our algorithms by showing that when running on digit recognition data, they obtain a high prediction accuracy even when the learner gets to see only four pixels of each image.


An approximate analytical (structural) superposition in terms of two, or more, "alfa"-circuits of the same topology: Pt. 2 - the "internal circuit mechanism"

Abstract: This is the second part, after [1], of the research devoted to analysis of 1-ports composed of similar conductors ("f-circuits") described by the characteristic i = f(v) of a polynomial type. This analysis is performed by means of the power-law "alfa"-circuits" introduced in [2], for which f(v) ~ v^"alfa". The f-circuits are constructed from the "alfa"-circuits of the same topology, with the proper "alfa", so that the given topology is kept, and 'f' is an additive function of the connection. Explaining the situation described in detail in [1], we note and analyze a simple "circuit mechanism" that causes the difference between the input current of the f-circuit and the sum of the input currents of the f-circuits before the composition to be relatively small. The case of two degrees, f(v) = Dmv^m + Dnv^n, m unequal n, is treated in the main proofs. Some simulations are presented, and some boundaries for the error of the superposition are found. The cases of f(.) being a polynomial of the third or fourth degrees are finally briefly considered.


LIKWID: A lightweight performance-oriented tool suite for x86 multicore environments

Abstract: Exploiting the performance of today's processors requires intimate knowledge of the microarchitecture as well as an awareness of the ever-growing complexity in thread and cache topology. LIKWID is a set of command-line utilities that addresses four key problems: Probing the thread and cache topology of a shared-memory node, enforcing thread-core affinity on a program, measuring performance counter metrics, and toggling hardware prefetchers. An API for using the performance counting features from user code is also included. We clearly state the differences to the widely used PAPI interface. To demonstrate the capabilities of the tool set we show the influence of thread pinning on performance using the well-known OpenMP STREAM triad benchmark, and use the affinity and hardware counter tools to study the performance of a stencil code specifically optimized to utilize shared caches on multicore chips.


Understanding periodicity and regularity of nodal encounters in mobile networks: A spectral analysis

Abstract: Study on human mobility is gaining increasing attention from the research community with its multiple applications to use in mobile networks, particularly for the purpose of message delivery in the Delay Tolerant Networks. To better understand the potential of mobile nodes as message relays, our study investigates the encounter pattern of mobile devices. Specifically, we examine the extensive network traces that reflect mobility of communication devices. We analyze the periodicity in encounter pattern by using power spectral analysis. Strong periodicity was observed among rarely encountering mobile nodes while the periodicity was weaker among frequently encountering nodes. Further, we present a method to search regularly encountering pairs and discuss the findings. To our knowledge, we are the first to analyze the periodicity of encounter pattern with large network traces, which is a critical basis for designing an efficient delivery scheme using mobile nodes.


An Intelligent Call Admission Control Decision Mechanism for Wireless Networks

Abstract: The Call admission control (CAC) is one of the Radio Resource Management (RRM) techniques plays instrumental role in ensuring the desired Quality of Service (QoS) to the users working on different applications which have diversified nature of QoS requirements. This paper proposes a fuzzy neural approach for call admission control in a multi class traffic based Next Generation Wireless Networks (NGWN). The proposed Fuzzy Neural Call Admission Control (FNCAC) scheme is an integrated CAC module that combines the linguistic control capabilities of the fuzzy logic controller and the learning capabilities of the neural networks .The model is based on Recurrent Radial Basis Function Networks (RRBFN) which have better learning and adaptability that can be used to develop the intelligent system to handle the incoming traffic in the heterogeneous network environment. The proposed FNCAC can achieve reduced call blocking probability keeping the resource utilisation at an optimal level. In the proposed algorithm we have considered three classes of traffic having different QoS requirements. We have considered the heterogeneous network environment which can effectively handle this traffic. The traffic classes taken for the study are Conversational traffic, Interactive traffic and back ground traffic which are with varied QoS parameters. The paper also presents the analytical model for the CAC .The paper compares the call blocking probabilities for all the three types of traffic in both the models. The simulation results indicate that compared to Fuzzy logic based CAC, Conventional CAC, The simulation results are optimistic and indicates that the proposed FNCAC algorithm performs better where the call blocking probability is minimal when compared to other two methods.


New Visual Cryptography Algorithm For Colored Image

Abstract: Visual Cryptography is a special encryption technique to hide information in images, which divide secret image into multiple layers. Each layer holds some information. The receiver aligns the layers and the secret information is revealed by human vision without any complex computation. The proposed algorithm is for color image, that presents a system which takes four pictures as an input and generates three images which correspond to three of the four input pictures. The decoding requires only selecting some subset of these 3 images, making transparencies of them, and stacking them on top of each other, so the forth picture is reconstructed by printing the three output images onto transparencies and stacking them together. The reconstructed image achieved in same size with original secret image.


Maintainability Estimation Model for Object-Oriented Software in Design Phase (MEMOOD)

Abstract: Measuring software maintainability early in the development life cycle, especially at the design phase, may help designers to incorporate required enhancement and corrections for improving maintainability of the final software. This paper developed a multivariate linear model 'Maintainability Estimation Model for Object-Oriented software in Design phase' (MEMOOD), which estimates the maintainability of class diagrams in terms of their understandability and modifiability. While, in order to quantify class diagram's understandability and modifiability the paper further developed two more multivariate models. These two models use design level object-oriented metrics, to quantify understandability and modifiability of class diagram. Such early quantification of maintainability provides an opportunity to improve the maintainability of class diagram and consequently the maintainability of final software. All the three models have been validated through appropriate statistical measures and contextual interpretation has been drawn.


Deblured Gaussian Blurred Images

Abstract: This paper attempts to undertake the study of Restored Gaussian Blurred Images. by using four types of techniques of deblurring image as Wiener filter, Regularized filter, Lucy Richardson deconvlutin algorithm and Blind deconvlution algorithm with an information of the Point Spread Function (PSF) corrupted blurred image with Different values of Size and Alfa and then corrupted by Gaussian noise. The same is applied to the remote sensing image and they are compared with one another, So as to choose the base technique for restored or deblurring image.This paper also attempts to undertake the study of restored Gaussian blurred image with no any information about the Point Spread Function (PSF) by using same four techniques after execute the guess of the PSF, the number of iterations and the weight threshold of it. To choose the base guesses for restored or deblurring image of this techniques.


Plagiarism Detection Using Graph-Based Representation

Abstract: Plagiarism of material from the Internet is a widespread and growing problem. Several methods used to detect the plagiarism and similarity between the source document and suspected documents such as fingerprint based on character or n-gram. In this paper, we discussed a new method to detect the plagiarism based on graph representation; however, Preprocessing for each document is required such as breaking down the document into its constituent sentences. Segmentation of each sentence into separated terms and stop word removal. We build the graph by grouping each sentence terms in one node, the resulted nodes are connected to each other based on order of sentence within the document, all nodes in graph are also connected to top level node "Topic Signature". Topic signature node is formed by extracting the concepts of each sentence terms and grouping them in such node. The main advantage of the proposed method is the topic signature which is main entry for the graph is used as quick guide to the relevant nodes. which should be considered for the comparison between source documents and suspected one. We believe the proposed method can achieve a good performance in terms of effectiveness and efficiency.


Improving Supply Chain Coordination by Linking Dynamic Procurement Decision to Multi-Agent System

Abstract: The Internet has changed the way business is conducted in many ways. For example, in the field of procurement, the possibility to directly interact with a trading partner has given rise to new mechanisms in the supply chain management. One such interactive dynamic procurement, which lets both buyer and seller software agents bid by potential buyer agents instead of static procurement by vendors. Dynamic procurement decision could provide the buying and selling channel to buyer, to avoid occurring condition that seller could not deliver on the contract promise. Using NYOP(Name Your Own Price) to be the core of dynamic procurement negotiation algorithm sets up multi-agent dynamic supply chain system, to present the DSINs(Dynamic Supply Chain Information Networks) by JADE, and to present the dynamic supply chain logistic simulation by eM-Plant. Finally, evaluating supply chain performance with supply chain performance metrics (such as bullwhip, fill rate), to be the reference of enterprise making deciding in the future.


Crowd simulation influenced by agent's socio-psychological state

Abstract: The aim our work is to create virtual humans as intelligent entities, which includes approximate the maximum as possible the virtual agent animation to the natural human behavior. In order to accomplish this task, our agent must be capable to interact with the environment, interacting with objects and other agents. The virtual agent needs to act as real person, so he should be capable to extract semantic information from the geometric model of the world where he is inserted, based on his own perception, and he realizes his own decision. The movement of the individuals is representing by the combination of two approaches of movement which are, the social force model and the based-rule model. These movements are influenced by a set of socio-psychological rules to give a more realistic result.


Combination of Subtractive Clustering and Radial Basis Function in Speaker Identification

Abstract: Speaker identification is the process of determining which registered speaker provides a given utterance. Speaker identification required to make a claim on the identity of speaker from the Ns trained speaker in its user database. In this study, we propose the combination of clustering algorithm and the classification technique - subtractive and Radial Basis Function (RBF). The proposed technique is chosen because RBF is a simpler network structures and faster learning algorithm. RBF finds the input to output map using the local approximators which will combine the linear of the approximators and cause the linear combiner have few weights. Besides that, RBF neural network model using subtractive clustering algorithm for selecting the hidden node centers, which can achieve faster training speed. In the meantime, the RBF network was trained with a regularization term so as to minimize the variances of the nodes in the hidden layer and perform more accu-rate prediction.


Crosstalk Noise Modeling for RC and RLC interconnects in Deep Submicron VLSI Circuits

Abstract: The crosstalk noise model for noise constrained interconnects optimization is presented for RC interconnects. The proposed model has simple closed-form expressions, which is capable of predicting the noise amplitude and the noise pulse width of an RC interconnect as well as coupling locations (near-driver and near-receiver) on victim net. This paper also presents a crosstalk noise model for both identical and non identical coupled resistance-inductance-capacitance (RLC) interconnects, which is developed based on a decoupling technique exhibiting an average error of 6.8% as compared to SPICE. The crosstalk noise model, together with a proposed concept of effective mutual inductance, is applied to evaluate the effectiveness of the shielding technique.


Visual Infrared Video Fusion for Night Vision using Background Estimation

Abstract: Video fusion is a process that combines visual data from different sensors to obtain a single composite video preserving the information of the sources. The availability of a system, enhancing human ability to perceive the observed scenario, is crucial to improve the performance of a surveillance system. The infrared (IR) camera captures thermal image of object in night-time environment, when only limited visual information can be captured by RGB camera. The fusion of data recorded by an IR sensor and a visible RGB camera can produce information otherwise not obtainable by viewing the sensor outputs separately. In this paper we consider the problem of fusing two video streams acquired by an RGB camera and an IR sensor. The pedestrians, distinctly captured by IR video, are separated and fused with the RGB video. The algorithms implemented involve estimation of the background, followed by detection of object from the IR Video, after necessary denoising. Finally a suitable fusion algorithm is employed to combine the extracted pedestrians with the visual output. The obtained results clearly demonstrate the effectiveness of the proposed video fusion scheme, for night vision.


Handling Overload Conditions In High Performance Trustworthy Information Retrieval Systems

Abstract: Web search engines retrieve a vast amount of information for a given search query. But the user needs only trustworthy and high-quality information from this vast retrieved data. The response time of the search engine must be a minimum value in order to satisfy the user. An optimum level of response time should be maintained even when the system is overloaded. This paper proposes an optimal Load Shedding algorithm which is used to handle overload conditions in real-time data stream applications and is adapted to the Information Retrieval System of a web search engine. Experiment results show that the proposed algorithm enables a web search engine to provide trustworthy search results to the user within an optimum response time, even during overload conditions.


BiLingual Information Retrieval System for English and Tamil

Abstract: This paper addresses the design and implementation of BiLingual Information Retrieval system on the domain, Festivals. A generic platform is built for BiLingual Information retrieval which can be extended to any foreign or Indian language working with the same efficiency. Search for the solution of the query is not done in a specific predefined set of standard languages but is chosen dynamically on processing the user's query. This paper deals with Indian language Tamil apart from English. The task is to retrieve the solution for the user given query in the same language as that of the query. In this process, a Ontological tree is built for the domain in such a way that there are entries in the above listed two languages in every node of the tree. A Part-Of-Speech (POS) Tagger is used to determine the keywords from the given query. Based on the context, the keywords are translated to appropriate languages using the Ontological tree. A search is performed and documents are retrieved based on the keywords. With the use of the Ontological tree, Information Extraction is done. Finally, the solution for the query is translated back to the query language (if necessary) and produced to the user.


A Metrics Based Model for Understandability Quantification

Abstract: Software developers and maintainers need to read and understand source programs and other software artifacts. The increase in size and complexity of software drastically affects several quality attributes, especially understandability and maintainability. False interpretation often leads to ambiguities, misunderstanding and hence to faulty development results. Despite the fact that software understandability is vital and one of the most significant components of the software development process, it is poorly managed. This is mainly due to the lack of its proper management and control. The paper highlights the importance of understandability in general and as a factor of software testability. Two major contributions are made in the paper. A relation between testability factors and object oriented characteristics has been established as a first contribution. In second contribution, a model has been proposed for estimating understandability of object oriented software using design metrics. In addition, the proposed model has been validated using experimental try-out.


Audio enabled information extraction system for cricket and hockey domains

Abstract: The proposed system aims at the retrieval of the summarized information from the documents collected from web based search engine as per the user query related to cricket and hockey domain. The system is designed in a manner that it takes the voice commands as keywords for search. The parts of speech in the query are extracted using the natural language extractor for English. Based on the keywords the search is categorized into 2 types: - 1.Concept wise - information retrieved to the query is retrieved based on the keywords and the concept words related to it. The retrieved information is summarized using the probabilistic approach and weighted means algorithm.2.Keyword search - extracts the result relevant to the query from the highly ranked document retrieved from the search by the search engine. The relevant search results are retrieved and then keywords are used for summarizing part. During summarization it follows the weighted and probabilistic approaches in order to identify the data comparable to the keywords extracted. The extracted information is then refined repeatedly through the aggregation process to reduce redundancy. Finally the resultant data is submitted to the user in the form of audio output.


Mobile Zigbee Sensor Networks

Abstract: OPNET Modeler accelerates network R&D and improves product quality through high-fidelity modeling and scalable simulation. It provides a virtual environment for designing protocols and devices, and for testing and demonstrating designs in realistic scenarios prior to production. OPNET Modeler supports 802.15.4 standard and has been used to make a model of PAN. Iterations have been performed by changing the Power of the transmitter and the throughput will has been analyzed to arrive at optimal values.An energy-efficient wireless home network based on IEEE 802.15.4, a novel architecture has been proposed. In this architecture, all nodes are classified into stationary nodes and mobile nodes according to the functionality of each node. Mobile nodes are usually battery-powered, and therefore need low-power operation. In order to improve power consumption of mobile nodes, effective handover sequence based on MAC broadcast and transmission power control based on LQ (link quality) are employed. Experimental results demonstrate that by using the proposed architecture, communication time and power consumption of mobile nodes can be reduced by 1.2 seconds and 42.8%, respectively.


Effect of Crosstalk on Permutation in Optical Multistage Interconnection Networks

Abstract: Optical MINs hold great promise and have advantages over their electronic networks.they also hold their own challenges. More research has been done on Electronic Multistage Interconnection Networks, (EMINs) but these days optical communication is a good networking choice to meet the increasing demands of high-performance computing communication applications for high bandwidth applications. The electronic Multistage Interconnection Networks (EMINs) and the Optical Multistage Interconnection Networks (OMINs) have many similarities, but there are some fundamental differences between them such as the optical-loss during switching and the crosstalk problem in the optical switches. To reduce the negative effect of crosstalk, various approaches which apply the concept of dilation in either the space or time domain have been proposed. With the space domain approach, extra SEs are used to ensure that at most one input and one output of every SE will be used at any given time. For an Optical network without crosstalk, it is needed to divide the messages into several groups, and then deliver the messages using one time slot (pass) for each group, which is called the time division multiplexing. This Paper discusses the permutation passability behavior of optical MINs. The bandwidth of optical MINs with or without crosstalk has also been explained. The results thus obtained shows that the performance of the networks improves by allowing crosstalk to some extent.


An Efficient Watermarking Algorithm to Improve Payload and Robustness without Affecting Image Perceptual Quality

Abstract: Capacity, Robustness, & Perceptual quality of watermark data are very important issues to be considered. A lot of research is going on to increase these parameters for watermarking of the digital images, as there is always a tradeoff among them. . In this paper an efficient watermarking algorithm to improve payload and robustness without affecting perceptual quality of image data based on DWT is discussed. The aim of the paper is to employ the nested watermarks in wavelet domain which increases the capacity and ultimately the robustness against attacks and selection of different scaling factor values for LL & HH bands and during embedding not to create the visible artifacts in the original image and therefore the original and watermarked image is similar.


Mobile Commerce and Applications: An Exploratory Study and Review

Abstract: Mobile commerce is enabling the development of additional revenue streams for organizations through the delivery of chargeable mobile services. According to the European Information Technology Observatory, the total amount of revenue generated by mobile commerce was reported to be less than {\pounds}9 million in the United Kingdom in 2001. By 2005 this had, at least, doubled and more recent industry forecasts project significant global growth in this area. Mobile commerce creates a range of business opportunities and new revenue streams for businesses across industry sectors via the deployment of innovative services, applications and associated information content. This paper presents a review of mobile commerce business models and their importance for the creation of mobile commerce solutions.


Preserving Privacy and Sharing the Data in Distributed Environment using Cryptographic Technique on Perturbed data

Abstract: The main objective of data mining is to extract previously unknown patterns from large collection of data. With the rapid growth in hardware, software and networking technology there is outstanding growth in the amount data collection. Organizations collect huge volumes of data from heterogeneous databases which also contain sensitive and private information about and individual .The data mining extracts novel patterns from such data which can be used in various domains for decision making .The problem with data mining output is that it also reveals some information, which are considered to be private and personal. Easy access to such personal data poses a threat to individual privacy. There has been growing concern about the chance of misusing personal information behind the scene without the knowledge of actual data owner. Privacy is becoming an increasingly important issue in many data mining applications in distributed environment. Privacy preserving data mining technique gives new direction to solve this problem. PPDM gives valid data mining results without learning the underlying data values .The benefits of data mining can be enjoyed, without compromising the privacy of concerned individuals. The original data is modified or a process is used in such a way that private data and private knowledge remain private even after the mining process. In this paper we have proposed a framework that allows systemic transformation of original data using randomized data perturbation technique and the modified data is then submitted as result of client's query through cryptographic approach. Using this approach we can achieve confidentiality at client as well as data owner sites. This model gives valid data mining results for analysis purpose but the actual or true data is not revealed.


Intelligent System for Speaker Identification using Lip features with PCA and ICA

Abstract: Biometric authentication techniques are more consistent and efficient than conventional authentication techniques and can be used in monitoring, transaction authentication, information retrieval, access control, forensics, etc. In this paper, we have presented a detailed comparative analysis between Principle Component Analysis (PCA) and Independent Component Analysis (ICA) which are used for feature extraction on the basis of different Artificial Neural Network (ANN) such as Back Propagation (BP), Radial Basis Function (RBF) and Learning Vector Quantization (LVQ). In this paper, we have chosen "TULIPS1 database, (Movellan, 1995)" which is a small audiovisual database of 12 subjects saying the first 4 digits in English for the incorporation of above methods. The six geometric lip features i.e. height of the outer corners of the mouth, width of the outer corners of the mouth, height of the inner corners of the mouth, width of the inner corners of the mouth, height of the upper lip, and height of the lower lip which extracts the identity relevant information are considered for the research work. After the comprehensive analysis and evaluation a maximum of 91.07% accuracy in speaker recognition is achieved using PCA and RBF and 87.36% accuracy is achieved using ICA and RBF. Speaker identification has a wide scope of applications such as access control, monitoring, transaction authentication, information retrieval, forensics, etc.


Prediction of Retained Capacity and EODV of Li-ion Batteries in LEO Spacecraft Batteries

Abstract: In resent years ANN is widely reported for modeling in different areas of science including electro chemistry. This includes modeling of different technological batteries such as lead acid battery, Nickel cadmium batteries etc. Lithium ion batteries are advance battery technology which satisfy most of the space mission requirements. Low earth orbit (LEO)space craft batteries undergo large number of charge discharge cycles (about 25000 cycles)compared to other ground level or space applications. This study is indented to develop ANN model for about 25000 cycles, cycled under various temperature, Depth Of Discharge (DOD) settings with constant charge voltage limit to predict the retained capacity and End of Discharge Voltage (EODV). To extract firm conclusion and distinguish the capability of ANN method, the predicted values are compared with experimental result by statistical method and Bland Altman plot.


Survey and Comparison of Optical Switch Fabrication Techniques and Architectures

Abstract: The main issue in the optical transmission is switching speed. The optical packet switching faces many significant challenges in processing and buffering. The generalized multilevel protocol switching seeks to eliminate the asynchronous transfer mode and synchronous optical network layer, hence the implementation of IP over WDM (wave length division multiplexing). Optical burst switching attempts to minimize the need for processing and buffering by aggregating flow of data packets in to burst. In this paper there is an extensive overview on current technologies and techniques concerning optical switching.


Finding and Classifying Critical Points of 2D Vector Fields: A Cell-Oriented Approach Using Group Theory

Abstract: We present a novel approach to finding critical points in cell-wise barycentrically or bilinearly interpolated vector fields on surfaces. The Poincar\e index of the critical points is determined by investigating the qualitative behavior of 0-level sets of the interpolants of the vector field components in parameter space using precomputed combinatorial results, thus avoiding the computation of the Jacobian of the vector field at the critical points in order to determine its index. The locations of the critical points within a cell are determined analytically to achieve accurate results. This approach leads to a correct treatment of cases with two first-order critical points or one second-order critical point of bilinearly interpolated vector fields within one cell, which would be missed by examining the linearized field only. We show that for the considered interpolation schemes determining the index of a critical point can be seen as a coloring problem of cell edges. A complete classification of all possible colorings in terms of the types and number of critical points yielded by each coloring is given using computational group theory. We present an efficient algorithm that makes use of these precomputed classifications in order to find and classify critical points in a cell-by-cell fashion. Issues of numerical stability, construction of the topological skeleton, topological simplification, and the statistics of the different types of critical points are also discussed.


MIREX: MapReduce Information Retrieval Experiments

Abstract: We propose to use MapReduce to quickly test new retrieval approaches on a cluster of machines by sequentially scanning all documents. We present a small case study in which we use a cluster of 15 low cost ma- chines to search a web crawl of 0.5 billion pages showing that sequential scanning is a viable approach to running large-scale information retrieval experiments with little effort. The code is available to other researchers at: http://mirex.sourceforge.net


Multiplication of sparse Laurent polynomials and Poisson series on modern hardware architectures

Abstract: In this paper we present two algorithms for the multiplication of sparse Laurent polynomials and Poisson series (the latter being algebraic structures commonly arising in Celestial Mechanics from the application of perturbation theories). Both algorithms first employ the Kronecker substitution technique to reduce multivariate multiplication to univariate multiplication, and then use the schoolbook method to perform the univariate multiplication. The first algorithm, suitable for moderately-sparse multiplication, uses the exponents of the monomials resulting from the univariate multiplication as trivial hash values in a one dimensional lookup array of coefficients. The second algorithm, suitable for highly-sparse multiplication, uses a cache-optimised hash table which stores the coefficient-exponent pairs resulting from the multiplication using the exponents as keys. Both algorithms have been implemented with attention to modern computer hardware architectures. Particular care has been devoted to the efficient exploitation of contemporary memory hierarchies through cache-blocking techniques and cache-friendly term ordering. The first algorithm has been parallelised for shared-memory multicore architectures, whereas the second algorithm is in the process of being parallelised. We present benchmarks comparing our algorithms to the routines of other computer algebra systems, both in sequential and parallel mode.


Highway Mobility and Vehicular Ad-Hoc Networks in NS-3

Abstract: The study of vehicular ad-hoc networks (VANETs) requires efficient and accurate simulation tools. As the mobility of vehicles and driver behavior can be affected by network messages, these tools must include a vehicle mobility model integrated with a quality network simulator. We present the first implementation of a well-known vehicle mobility model to ns-3, the next generation of the popular ns-2 networking simulator. Vehicle mobility and network communication are integrated through events. User-created event handlers can send network messages or alter vehicle mobility each time a network message is received and each time vehicle mobility is updated by the model. To aid in creating simulations, we have implemented a straight highway model that manages vehicle mobility, while allowing for various user customizations. We show that the results of our implementation of the mobility model matches that of the model's author and provide an example of using our implementation in ns-3.


The Accuracy of Tree-based Counting in Dynamic Networks

Abstract: Tree-based protocols are ubiquitous in distributed systems. They are flexible, they perform generally well, and, in static conditions, their analysis is mostly simple. Under churn, however, node joins and failures can have complex global effects on the tree overlays, making analysis surprisingly subtle. To our knowledge, few prior analytic results for performance estimation of tree based protocols under churn are currently known. We study a simple Bellman-Ford-like protocol which performs network size estimation over a tree-shaped overlay. A continuous time Markov model is constructed which allows key protocol characteristics to be estimated, including the expected number of nodes at a given (perceived) distance to the root and, for each such node, the expected (perceived) size of the subnetwork rooted at that node. We validate the model by simulation, using a range of network sizes, node degrees, and churn-to-protocol rates, with convincing results.


A Simple Polynomial Algorithm for the Longest Path Problem on Cocomparability Graphs

Abstract: Given a graph $G$, the longest path problem asks to compute a simple path of $G$ with the largest number of vertices. This problem is the most natural optimization version of the well known and well studied Hamiltonian path problem, and thus it is NP-hard on general graphs. However, in contrast to the Hamiltonian path problem, there are only few restricted graph families such as trees and some small graph classes where polynomial algorithms for the longest path problem have been found. Recently it has been shown that this problem can be solved in polynomial time on interval graphs by applying dynamic programming to a characterizing ordering of the vertices of the given graph \cite{longest-int-algo}, thus answering an open question. In the present paper, we provide the first polynomial algorithm for the longest path problem on a much greater class, namely on cocomparability graphs. Our algorithm uses a similar - but essentially simpler - dynamic programming approach, which is applied to a Lexicographic Depth First Search (LDFS) characterizing ordering of the vertices of a cocomparability graph. Therefore, our results provide evidence that this general dynamic programming approach can be used in a more general setting, leading to efficient algorithms for the longest path problem on greater classes of graphs. LDFS has recently been introduced in \cite{Corneil-LDFS08}. Since then, a similar phenomenon of extending an existing interval graph algorithm to cocomparability graphs by using an LDFS preprocessing step has also been observed for the minimum path cover problem \cite{Corneil-MPC}. Therefore, more interestingly, our results also provide evidence that cocomparability graphs present an interval graph structure when they are considered using an LDFS ordering of their vertices, which may lead to other new and more efficient combinatorial algorithms.


In Quest of the Better Mobile Broadband Solution for South Asia Taking WiMAX and LTE into Consideration

Abstract: Internet generation is growing accustomed to having broadband access wherever they go and not just at home or in the office, which turns mobile broadband into a reality. This paper aims to look for a suitable mobile broadband solution in the South Asian region through comparative analysis in various perspectives. Both WiMAX and LTE are 4G technologies designed to move data rather than voice having IP networks based on OFDM technology. Proving competency in various significant aspects WiMAX and LTE already have made a strong position in telecommunication industry. Again, because of certain similarities in technology; they aren't like technological rivals as of GSM and CDMA. But still they are treated as opponents and viewed as a major threat in case of the flourishing of each other. Such view point is surely not conducive for getting the best out of them. In this paper various aspects and applications of WiMAX and LTE for deployment have been analyzed. South Asia being the residence of an enormous number of people presents an exciting opportunity for mobile operators, developers and internet service providers. So, every consideration that has been made here also correlates successfully with south Asia i.e. how mass people of this region may be benefited from it. As a result, it might be regarded as a good source in case of making major BWA deployment decisions in this region. Besides these, it also opens the path for further research and thinking in this issue.


Measuring Data and VoIP Traffic in WiMAX Networks

Abstract: Due to its large coverage area, low cost of deployment and high speed data rates, WiMAX is a promising technology for providing wireless last-mile connectivity. Physical and MAC layer of this technology refer to the IEEE 802.16e standard, which defines 5 different data delivery service classes that can be used in order to satisfy Quality of Service (QoS) requirements of different applications, such as VoIP, videoconference, FTP, Web, etc. The main aim of the paper is to examine a case of QoS deployment over a cellular WiMAX network. In particular, the paper compares the performance obtained using two different QoS configurations differing from the delivery service class used to transport VoIP traffic, i.e. UGS or ertPS. Results indicate that for delay-sensitive traffic that fluctuates beyond its nominal rate, having the possibility to give back some of its reserved bandwidth, ertPS has the advantage to permit the transmission of BE traffic.


Optimal Crosstalk Detection and Localization Method for Optical Time Division Multiplexed Transmission Systems

Abstract: All-Optical Network (AON) is a network where the user-network interface is optical and the data does undergo optical to electrical conversion within the network. AONs are attractive because they promise very high rates, flexible switching and broad application support. There are two technologies for AON: Wavelength Division Multiplexed (WDM) and Optical Time Division Multiplexed (OTDM). OTDM transmission systems are becoming increasingly important as one of the key technologies satisfying the growing demand for large capacity optical networks. Although OTDM has several advantages in terms of operation system, such as natural accommodation of higher bit rate payloads, it introduces many security vulnerabilities, which do not exist in traditional networks. One of the serious problems with OTDM is the fact that optical crosstalk is additive, and thus the aggregate effect of crosstalk over a whole all-optical network (AON) may be more nefarious than a single point of crosstalk. This is because crosstalk can spread rapidly through the network, causing additional awkward failures and triggering multiple undesirable alarms. This results in the continuous monitoring and identification of the impairments becoming challenging in the event of transmission failures. In this paper we propose a novel approach for detecting and localizing crosstalk in OTDM transmission systems that can participate in some tasks for fault management in optical network.


Investigation on QoS of Campus-wide WiFi Networks

Abstract: WiFi is widely implemented in campus wide including administrative, teaching and student's accommodation. Wireless communications are associated with interconnect devices which includes cellular networks, infrared, bluetooth and WiFi enabled devices. It involves mobility and freedom of assessing information anytime and anywhere. A study on WiFi networks in a campus environment is presented in this paper. The aim of the research was to investigate the connectivity problems to WiFi networks. The study includes WiFi performance analysis as well as network auditing. Channel overlapping and saturation condition were some of the problems encountered. Different types of software were used for analyzing the results.


Modelling and Design of a Microstrip Band-Pass Filter Using Space Mapping Techniques

Abstract: Determination of design parameters based on electromagnetic simulations of microwave circuits is an iterative and often time-consuming procedure. Space mapping is a powerful technique to optimize such complex models by efficiently substituting accurate but expensive electromagnetic models, fine models, with fast and approximate models, coarse models. In this paper, we apply two space mapping, an explicit space mapping as well as an implicit and response residual space mapping, techniques to a case study application, a microstrip band-pass filter. First, we model the case study application and optimize its design parameters, using explicit space mapping modelling approach. Then, we use implicit and response residual space mapping approach to optimize the filter's design parameters. Finally, the performance of each design methods is evaluated. It is shown that the use of above-mentioned techniques leads to achieving satisfactory design solutions with a minimum number of computationally expensive fine model evaluations.


Clustering of Content Supporting Computer Mediated Courseware Development

Abstract: Computer Mediated Courseware (CMC) has been developed so far for individual courses considering single or multiple text books. A group of courseware can be developed by using multiple text books and in this case, it is a requirement to cluster the contents of different books to form a generalized clustered content. No work has been found to develop courseware applying generalized clustered content. We have proposed a clustering of content supporting computer mediated courseware development based on data mining techniques to construct a hierarchical general structure of a group of courseware combining the individual structure of a set of books. The clustering will help the courseware developer to dynamically allocate contents to develop different courses using a group of books. The authors have applied this methodology for different level of courses on database. The methodology is generalized and can be applied to any other courses.


Revealing Method for the Intrusion Detection System

Abstract: The goal of an Intrusion Detection is inadequate to detect errors and unusual activity on a network or on the hosts belonging to a local network by monitoring network activity. Algorithms for building detection models are broadly classified into two categories, Misuse Detection and Anomaly Detection. The proposed approach should be taken into account, as the security system violations caused by both incompliance with the security policy and attacks on the system resulting in the need to describe models. However, it is based on unified mathematical formalism which is provided for subsequent merger of the models. The above formalism in this paper presents a state machine describing the behavior of a system subject. The set of intrusion description models is used by the evaluation module and determines the likelihood of undesired actions the system is capable of detecting. The number of attacks which are not described by models determining the completeness of detection by the IDS linked to the ability of detecting security violations.


Wireless sensors networks MAC protocols analysis

Abstract: Wireless sensors networks performance are strictly related to the medium access mechanism. An effective one, require non-conventional paradigms for protocol design due to several constraints. An adequate equilibrium between communication improvement and data processing capabilities must be accomplished. To achieve low power operation, several MAC protocols already proposed for WSN. The aim of this paper is to survey and to analyze the most energy efficient MAC protocol in order to categorize them and to compare their performances. Furthermore we have implemented some of WSN MAC protocol under OMNET++ with the purpose to evaluate their performances.


Video shot boundary detection using motion activity descriptor

Abstract: This paper focus on the study of the motion activity descriptor for shot boundary detection in video sequences. We interest in the validation of this descriptor in the aim of its real time implementation with reasonable high performances in shot boundary detection. The motion activity information is extracted in uncompressed domain based on adaptive rood pattern search (ARPS) algorithm. In this context, the motion activity descriptor was applied for different video sequence.


Mobility Prediction in Wireless Ad Hoc Networks using Neural Networks

Abstract: Mobility prediction allows estimating the stability of paths in a mobile wireless Ad Hoc networks. Identifying stable paths helps to improve routing by reducing the overhead and the number of connection interruptions. In this paper, we introduce a neural network based method for mobility prediction in Ad Hoc networks. This method consists of a multi-layer and recurrent neural network using back propagation through time algorithm for training.


Evaluation of Burst Loss Rate of an Optical Burst Switching (OBS) Network with Wavelength Conversion Capability

Abstract: This paper presents a new analytical model for calculating burst loss rate (BLR) in a slotted optical burst switched network. The analytical result leads to a framework which provides guidelines for optical burst switched networks. Wavelength converter is used for burst contention resolution. The effect of several design parameters such as burst arrival probability, wavelength conversion capability, number of slots per burst and number of wavelengths is incorporated on the above performance measure. We also extend the analytical result of BLR for different types of service classes where each service class has a reserved number of wavelengths in a network with fixed number of wavelengths. We also introduce an algorithm to calculate the resultant number of wavelength for each service classes depending on the various scenarios.


Performance Evaluation of Wavelength Routed Optical Network with Wavelength Conversion

Abstract: The rapid development of telecommunication networks is driven by user demands for new applications and advances in technologies. The explosive growth of the internet traffic is due to its use for collecting the information, communication, multimedia application, entertainment, etc. These applications are imposing a tremendous demand for bandwidth capacity on telecommunication network. The introduction of fiber optics had proved to meet the huge demand of bandwidth. These requirement can be meet by all optical network which is capable of transmitting enormous data at very high speed, around 50 Tera bits per seconds (Tbps) A wavelength conversion technique is addressed in this paper to reduced the blocking probability in wavelength routed networks. It is seen that the blocking probability of traffic requests decreases as the wavelength conversion factor increases. We explode the possibility for network with different size with variation in wavelength per link. In this work the evaluation of wavelength routed optical network with varying number of wavelength converters, different traffic types are carried out and results are shown that the blocking probability is minimum with 50% to 60% wavelength convertible nodes. Wavelength convertible nodes more than 60% are not showing much effect on reduction in blocking probability rather it results in increase in overall cost of network.


Conception and FPGA implementation of IEEE 802.11s mesh network MAC layer transmitter

Abstract: This paper proposes, a hardware implementation of Wireless Mesh Networks (WMN) medium Access Controller (MAC) layer transmitter. In the literature a lot of works are focused on WMN routing protocol as well as performance analysis and software integration of WMN units, however few works deals with WMN hardware implementation. In this field our contribution is to conceive and to implements on FPGA a WMN MAC transmitter module. Our implementation, written in hardware description language (HDL) is based on the IEEE 802.11 s standard. The hardware implementation retains a good performance in speed.


Chunky and Equal-Spaced Polynomial Multiplication

Abstract: Finding the product of two polynomials is an essential and basic problem in computer algebra. While most previous results have focused on the worst-case complexity, we instead employ the technique of adaptive analysis to give an improvement in many "easy" cases. We present two adaptive measures and methods for polynomial multiplication, and also show how to effectively combine them to gain both advantages. One useful feature of these algorithms is that they essentially provide a gradient between existing "sparse" and "dense" methods. We prove that these approaches provide significant improvements in many cases but in the worst case are still comparable to the fastest existing algorithms.


Verification of Object-Oriented Programs: a Transformational Approach

Abstract: We show that verification of object-oriented programs by means of the assertional method can be achieved in a simple way by exploiting a syntax-directed transformation from object-oriented programs to recursive programs. This transformation suggests natural proofs rules and its correctness helps us to establish soundness and relative completeness of the proposed proof system. One of the difficulties is how to properly deal in the assertion language with the instance variables and aliasing. The discussed programming language supports arrays, instance variables, failures and recursive methods with parameters.


Explicit Maximum Likelihood Loss Estimator in Multicast Tomography

Abstract: For the tree topology, previous studies show the maximum likelihood estimate (MLE) of a link/path takes a polynomial form with a degree that is one less than the number of descendants connected to the link/path. Since then, the main concern is focused on searching for methods to solve the high degree polynomial without using iterative approximation. An explicit estimator based on the Law of Large Numbers has been proposed to speed up the estimation. However, the estimate obtained from the estimator is not a MLE. When $n<\infty$, the estimate may be noticeable different from the MLE. To overcome this, an explicit MLE estimator is presented in this paper and a comparison between the MLE estimator and the explicit estimator proposed previously is presented to unveil the insight of the MLE estimator and point out the pitfall of the previous one.


Relating L-Resilience and Wait-Freedom via Hitting Sets

Abstract: The condition of t-resilience stipulates that an n-process program is only obliged to make progress when at least n-t processes are correct. Put another way, the live sets, the collection of process sets such that progress is required if all the processes in one of these sets are correct, are all sets with at least n-t processes. We show that the ability of arbitrary collection of live sets L to solve distributed tasks is tightly related to the minimum hitting set of L, a minimum cardinality subset of processes that has a non-empty intersection with every live set. Thus, finding the computing power of L is NP-complete. For the special case of colorless tasks that allow participating processes to adopt input or output values of each other, we use a simple simulation to show that a task can be solved L-resiliently if and only if it can be solved (h-1)-resiliently, where h is the size of the minimum hitting set of L. For general tasks, we characterize L-resilient solvability of tasks with respect to a limited notion of weak solvability: in every execution where all processes in some set in L are correct, outputs must be produced for every process in some (possibly different) participating set in L. Given a task T, we construct another task T_L such that T is solvable weakly L-resiliently if and only if T_L is solvable weakly wait-free.


Simulating Parallel Algorithms in the MapReduce Framework with Applications to Parallel Computational Geometry

Abstract: In this paper, we describe efficient MapReduce simulations of parallel algorithms specified in the BSP and PRAM models. We also provide some applications of these simulation results to problems in parallel computational geometry for the MapReduce framework, which result in efficient MapReduce algorithms for sorting, 1-dimensional all nearest-neighbors, 2-dimensional convex hulls, 3-dimensional convex hulls, and fixed-dimensional linear programming. For the case when reducers can have a buffer size of $B=O(n^\epsilon)$, for a small constant $\epsilon>0$, all of our MapReduce algorithms for these applications run in a constant number of rounds and have a linear-sized message complexity, with high probability, while guaranteeing with high probability that all reducer lists are of size $O(B)$.


A Data Cleansing Method for Clustering Large-scale Transaction Databases

Abstract: In this paper, we emphasize the need for data cleansing when clustering large-scale transaction databases and propose a new data cleansing method that improves clustering quality and performance. We evaluate our data cleansing method through a series of experiments. As a result, the clustering quality and performance were significantly improved by up to 165% and 330%, respectively.


Direct Proofs of Order Independence

Abstract: We establish a generic result concerning order independence of a dominance relation on finite games. It allows us to draw conclusions about order independence of various dominance relations in a direct and simple way.


On the Complexity of the $k$-Anonymization Problem

Abstract: We study the problem of anonymizing tables containing personal information before releasing them for public use. One of the formulations considered in this context is the $k$-anonymization problem: given a table, suppress a minimum number of cells so that in the transformed table, each row is identical to atleast $k-1$ other rows. The problem is known to be NP-hard and MAXSNP-hard; but in the known reductions, the number of columns in the constructed tables is arbitrarily large. However, in practical settings the number of columns is much smaller. So, we study the complexity of the practical setting in which the number of columns $m$ is small. We show that the problem is NP-hard, even when the number of columns $m$ is a constant ($m=3$). We also prove MAXSNP-hardness for this restricted version and derive that the problem cannot be approximated within a factor of (6238/6237). Our reduction uses alphabets $\Sigma$ of arbitrarily large size. A natural question is whether the problem remains NP-hard when both $m$ and $|\Sigma|$ are small. We prove that the $k$-anonymization problem is in $P$ when both $m$ and $|\Sigma|$ are constants.


On the comparison of plans: Proposition of an instability measure for dynamic machine scheduling

Abstract: On the basis of an analysis of previous research, we present a generalized approach for measuring the difference of plans with an exemplary application to machine scheduling. Our work is motivated by the need for such measures, which are used in dynamic scheduling and planning situations. In this context, quantitative approaches are needed for the assessment of the robustness and stability of schedules. Obviously, any `robustness' or `stability' of plans has to be defined w. r. t. the particular situation and the requirements of the human decision maker. Besides the proposition of an instability measure, we therefore discuss possibilities of obtaining meaningful information from the decision maker for the implementation of the introduced approach.


Indoor Positioning with Radio Location Fingerprinting

Abstract: An increasingly important requirement for many novel applications is sensing the positions of people, equipment, etc. GPS technology has proven itself as a successfull technology for positioning in outdoor environments but indoor no technology has yet gained a similar wide-scale adoption. A promising indoor positioning technique is radio-based location fingerprinting, having the major advantage of exploiting already existing radio infrastructures, like IEEE 802.11, which avoids extra deployment costs and effort. The research goal of this thesis is to address the limitations of current indoor location fingerprinting systems. In particular the aim is to advance location fingerprinting techniques for the challenges of handling heterogeneous clients, scalability to many clients, and interference between communication and positioning. The wireless clients used for location fingerprinting are heterogeneous even when only considering clients for the same technology. Heterogeneity is a challenge for location fingerprinting because it severely decreases the precision of location fingerprinting. To support many clients location fingerprinting has to address how to scale estimate calculation, measurement distribution, and distribution of position estimates. This is a challenge because of the number of calculations involved and the frequency of measurements and position updates. Positioning using location fingerprinting requires the measurement of, for instance, signal strength for nearby base stations. However, many wireless communication technologies block communication while collecting such measurements. This interference is a challenge because it is not desirable that positioning disables communication. An additional goal is to improve the conceptual foundation of location fingerprinting. A better foundation will aid researchers to better survey and design location fingerprinting systems.


VAGO method for the solution of elliptic second-order boundary value problems

Abstract: Mathematical physics problems are often formulated using differential oprators of vector analysis - invariant operators of first order, namely, divergence, gradient and rotor operators. In approximate solution of such problems it is natural to employ similar operator formulations for grid problems, too. The VAGO (Vector Analysis Grid Operators) method is based on such a methodology. In this paper the vector analysis difference operators are constructed using the Delaunay triangulation and the Voronoi diagrams. Further the VAGO method is used to solve approximately boundary value problems for the general elliptic equation of second order. In the convection-diffusion-reaction equation the diffusion coefficient is a symmetric tensor of second order.


Logical methods of object recognition on satellite images using spatial constraints

Abstract: A logical approach to object recognition on image is proposed. The main idea of the approach is to perform the object recognition as a logical inference on a set of rules describing an object shape.


Compiling Signal Processing Code embedded in Haskell via LLVM

Abstract: We discuss a programming language for real-time audio signal processing that is embedded in the functional language Haskell and uses the Low-Level Virtual Machine as back-end. With that framework we can code with the comfort and type safety of Haskell while achieving maximum efficiency of fast inner loops and full vectorisation. This way Haskell becomes a valuable alternative to special purpose signal processing languages.


Ontology-based inference for causal explanation

Abstract: We define an inference system to capture explanations based on causal statements, using an ontology in the form of an IS-A hierarchy. We first introduce a simple logical language which makes it possible to express that a fact causes another fact and that a fact explains another fact. We present a set of formal inference patterns from causal statements to explanation statements. We introduce an elementary ontology which gives greater expressiveness to the system while staying close to propositional reasoning. We provide an inference system that captures the patterns discussed, firstly in a purely propositional framework, then in a datalog (limited predicate) framework.


Revisiting LFSMs

Abstract: Linear Finite State Machines (LFSMs) are particular primitives widely used in information theory, coding theory and cryptography. Among those linear automata, a particular case of study is Linear Feedback Shift Registers (LFSRs) used in many cryptographic applications such as design of stream ciphers or pseudo-random generation. LFSRs could be seen as particular LFSMs without inputs. In this paper, we first recall the description of LFSMs using traditional matrices representation. Then, we introduce a new matrices representation with polynomial fractional coefficients. This new representation leads to sparse representations and implementations. As direct applications, we focus our work on the Windmill LFSRs case, used for example in the E0 stream cipher and on other general applications that use this new representation. In a second part, a new design criterion called diffusion delay for LFSRs is introduced and well compared with existing related notions. This criterion represents the diffusion capacity of an LFSR. Thus, using the matrices representation, we present a new algorithm to randomly pick LFSRs with good properties (including the new one) and sparse descriptions dedicated to hardware and software designs. We present some examples of LFSRs generated using our algorithm to show the relevance of our approach.