Efficient multicast data transfer with congestion control using dynamic source channels

Abstract: The most efficient receiver-driven multicast congestion control protocols use dynamic channels. This means that each group has a cyclic rate variation with a continuously decreasing phase. Despite promising results in terms of fairness, using efficiently these dynamic groups could be a challenging task for application programmers. This paper presents a sequencer which maps out application data to dynamic groups in an optimal way. Multiple applications such as file transfer or video streaming, can use this sequencer, thanks to a simple API usable with any buffer containing the most important data first. To evaluate this solution, we designed a file transfer software using a FEC encoding. Results show the sequencer optimal behavior and the file transfer efficiency, as a single download generates only little more overhead than TCP . Moreover, download time is almost independent of the number of receivers, and is already faster than TCP with 2 competing downloads.


Optimum Design of a 4x4 Planar Butler Matrix Array for WLAN Application

Abstract: In recent years, high-speed wireless communication is in vogue. In wireless communication systems, multipath fading, delay and interference occurres by reflection or diffraction. In a high-speed wireless communication, it becomes a necessary to separate desired signal from delay or interference signal. Thus to overcome these problems Smart antenna systems have been developed. Basically there are two types of smart antenna systems, one is Switched beam system and another Adaptive array system.This paper presents the optimum design of a 4x4 plannar Butler matrix array as a key component of a switched beam smart antenna system, operating at 5.2 GHz for WLAN with a dielectric substrate, FR4 of er =4.9 and h=1.6mm. Conception details, simulation results and measurements are also given for the components (microstrip antenna, hybrid couplers, cross-coupler, phase shifter) used to implement the matrix. In this dissertation, mathematical calculations for all the components using MATLAB is done and then every individual component is designed using the commercial software SONNET. Then these entire components have been combined on a single substrate and simulated using SONNET.


Polynomial Learning of Distribution Families

Abstract: The question of polynomial learnability of probability distributions, particularly Gaussian mixture distributions, has recently received significant attention in theoretical computer science and machine learning. However, despite major progress, the general question of polynomial learnability of Gaussian mixture distributions still remained open. The current work resolves the question of polynomial learnability for Gaussian mixtures in high dimension with an arbitrary fixed number of components. The result on learning Gaussian mixtures relies on an analysis of distributions belonging to what we call "polynomial families" in low dimension. These families are characterized by their moments being polynomial in parameters and include almost all common probability distributions as well as their mixtures and products. Using tools from real algebraic geometry, we show that parameters of any distribution belonging to such a family can be learned in polynomial time and using a polynomial number of sample points. The result on learning polynomial families is quite general and is of independent interest. To estimate parameters of a Gaussian mixture distribution in high dimensions, we provide a deterministic algorithm for dimensionality reduction. This allows us to reduce learning a high-dimensional mixture to a polynomial number of parameter estimations in low dimension. Combining this reduction with the results on polynomial families yields our result on learning arbitrary Gaussian mixtures in high dimensions.


Fr\'echet Distance Problems in Weighted Regions

Abstract: We discuss two versions of the Fr\'echet distance problem in weighted planar subdivisions. In the first one, the distance between two points is the weighted length of the line segment joining the points. In the second one, the distance between two points is the length of the shortest path between the points. In both cases, we give algorithms for finding a (1+epsilon)-factor approximation of the Fr\'echet distance between two polygonal curves. We also consider the Fr\'echet distance between two polygonal curves among polyhedral obstacles in R^3 and present a (1+epsilon)-factor approximation algorithm.


Born to be Wild: Using Communities of Practice as a Tool for Knowledge Management

Abstract: This paper looks at what happens when Communities of Practice are used as a tool for Knowledge Management. The original concept of a Community of Practice appears to have very little in common with the knowledge sharing communities found in Knowledge Management, which are based on a revised view of 'cultivated' communities. We examine the risks and benefits of cultivating Communities of Practice rather than leaving them 'in the wild'. The paper presents the findings from two years of research in a small microelectronics firm to provide some insights into the wild vs domesticated dichotomy and discusses the implications of attempting to tame Communities of Practice in this way.


Recursive Information Hiding in Visual Cryptography

Abstract: Visual Cryptography is a secret sharing scheme that uses the human visual system to perform computations. This paper presents a recursive hiding scheme for 3 out of 5 secret sharing. The idea used is to hide smaller secrets in the shares of a larger secret without an expansion in the size of the latter.


Graph Sparsification via Refinement Sampling

Abstract: A graph G'(V,E') is an \eps-sparsification of G for some \eps>0, if every (weighted) cut in G' is within (1\pm \eps) of the corresponding cut in G. A celebrated result of Benczur and Karger shows that for every undirected graph G, an \eps-sparsification with O(n\log n/\e^2) edges can be constructed in O(m\log^2n) time. Applications to modern massive data sets often constrain algorithms to use computation models that restrict random access to the input. The semi-streaming model, in which the algorithm is constrained to use \tilde O(n) space, has been shown to be a good abstraction for analyzing graph algorithms in applications to large data sets. Recently, a semi-streaming algorithm for graph sparsification was presented by Anh and Guha; the total running time of their implementation is \Omega(mn), too large for applications where both space and time are important. In this paper, we introduce a new technique for graph sparsification, namely refinement sampling, that gives an \tilde{O}(m) time semi-streaming algorithm for graph sparsification. Specifically, we show that refinement sampling can be used to design a one-pass streaming algorithm for sparsification that takes O(\log\log n) time per edge, uses O(\log^2 n) space per node, and outputs an \eps-sparsifier with O(n\log^3 n/\eps^2) edges. At a slightly increased space and time complexity, we can reduce the sparsifier size to O(n \log n/\e^2) edges matching the Benczur-Karger result, while improving upon the Benczur-Karger runtime for m=\omega(n\log^3 n). Finally, we show that an \eps-sparsifier with O(n \log n/\eps^2) edges can be constructed in two passes over the data and O(m) time whenever m =\Omega(n^{1+\delta}) for some constant \delta>0. As a by-product of our approach, we also obtain an O(m\log\log n+n \log n) time streaming algorithm to compute a sparse k-connectivity certificate of a graph.


FauxCrypt - A Method of Text Obfuscation

Abstract: Warnings have been raised about the steady diminution of privacy. More and more personal information, such as that contained electronic mail, is moving to cloud computing servers where it might be machine-searched and indexed. FauxCrypt is an algorithm for modification of a plaintext document that leaves it generally readable by a person but not readily searched or indexed by machine. The algorithm employs a dictionary substitution of selected words, and an obfuscating transposition of letters in other words. The obfuscation is designed to leave the words understandable, although they are badly spelled. FauxCrypt is free, open source software, with source code available.


Discrete geometric analysis of message passing algorithm on graphs

Abstract: We often encounter probability distributions given as unnormalized products of non-negative functions. The factorization structures are represented by hypergraphs called factor graphs. Such distributions appear in various fields, including statistics, artificial intelligence, statistical physics, error correcting codes, etc. Given such a distribution, computations of marginal distributions and the normalization constant are often required. However, they are computationally intractable because of their computational costs. One successful approximation method is Loopy Belief Propagation (LBP) algorithm. The focus of this thesis is an analysis of the LBP algorithm. If the factor graph is a tree, i.e. having no cycle, the algorithm gives the exact quantities. If the factor graph has cycles, however, the LBP algorithm does not give exact results and possibly exhibits oscillatory and non-convergent behaviors. The thematic question of this thesis is "How the behaviors of the LBP algorithm are affected by the discrete geometry of the factor graph?" The primary contribution of this thesis is the discovery of a formula that establishes the relation between the LBP, the Bethe free energy and the graph zeta function. This formula provides new techniques for analysis of the LBP algorithm, connecting properties of the graph and of the LBP and the Bethe free energy. We demonstrate applications of the techniques to several problems including (non) convexity of the Bethe free energy, the uniqueness and stability of the LBP fixed point. We also discuss the loop series initiated by Chertkov and Chernyak. The loop series is a subgraph expansion of the normalization constant, or partition function, and reflects the graph geometry. We investigate theoretical natures of the series. Moreover, we show a partial connection between the loop series and the graph zeta function.


Shallow Circuits with High-Powered Inputs

Abstract: A polynomial identity testing algorithm must determine whether an input polynomial (given for instance by an arithmetic circuit) is identically equal to 0. In this paper, we show that a deterministic black-box identity testing algorithm for (high-degree) univariate polynomials would imply a lower bound on the arithmetic complexity of the permanent. The lower bounds that are known to follow from derandomization of (low-degree) multivariate identity testing are weaker. To obtain our lower bound it would be sufficient to derandomize identity testing for polynomials of a very specific norm: sums of products of sparse polynomials with sparse coefficients. This observation leads to new versions of the Shub-Smale tau-conjecture on integer roots of univariate polynomials. In particular, we show that a lower bound for the permanent would follow if one could give a good enough bound on the number of real roots of sums of products of sparse polynomials (Descartes' rule of signs gives such a bound for sparse polynomials and products thereof). In this third version of our paper we show that the same lower bound would follow even if one could only prove a slightly superpolynomial upper bound on the number of real roots. This is a consequence of a new result on reduction to depth 4 for arithmetic circuits which we establish in a companion paper. We also show that an even weaker bound on the number of real roots would suffice to obtain a lower bound on the size of depth 4 circuits computing the permanent.


Computing in Coq with Infinite Algebraic Data Structures

Abstract: Computational content encoded into constructive type theory proofs can be used to make computing experiments over concrete data structures. In this paper, we explore this possibility when working in Coq with chain complexes of infinite type (that is to say, generated by infinite sets) as a part of the formalization of a hierarchy of homological algebra structures.


The stubborn problem is stubborn no more (a polynomial algorithm for 3-compatible colouring and the stubborn list partition problem)

Abstract: One of the driving problems in the CSP area is the Dichotomy Conjecture, formulated in 1993 by Feder and Vardi [STOC'93], stating that for any fixed relational structure G the Constraint Satisfaction Problem CSP(G) is either NP--complete or polynomial time solvable. A large amount of research has gone into checking various specific cases of this conjecture. One such variant which attracted a lot of attention in the recent years is the LIST MATRIX PARTITION problem. In 2004 Cameron et al. [SODA'04] classified almost all LIST MATRIX PARTITION variants for matrices of size at most four. The only case which resisted the classification became known as the STUBBORN PROBLEM. In this paper we show a result which enables us to finish the classification - thus solving a problem which resisted attacks for the last six years. Our approach is based on a combinatorial problem known to be at least as hard as the STUBBORN PROBLEM - the 3-COMPATIBLE COLOURING problem. In this problem we are given a complete graph with each edge assigned one of 3 possible colours and we want to assign one of those 3 colours to each vertex in such a way that no edge has the same colour as both of its endpoints. The tractability of the 3-COMPATIBLE COLOURING problem has been open for several years and the best known algorithm prior to this paper is due to Feder et al. [SODA'05] - a quasipolynomial algorithm with a n^O(log n / log log n) time complexity. In this paper we present a polynomial-time algorithm for the 3-COMPATIBLE COLOURING problem and consequently we prove a dichotomy for the k-COMPATIBLE COLOURING problem.


Bandwidth and Distortion Revisited

Abstract: In this paper we merge recent developments on exact algorithms for finding an ordering of vertices of a given graph that minimizes bandwidth (the BANDWIDTH problem) and for finding an embedding of a given graph into a line that minimizes distortion (the DISTORTION problem). For both problems we develop algorithms that work in O(9.363^n) time and polynomial space. For BANDWIDTH, this improves O^*(10^n) algorithm by Feige and Kilian from 2000, for DISTORTION this is the first polynomial space exact algorithm that works in O(c^n) time we are aware of. As a byproduct, we enhance the O(5^{n+o(n)})-time and O^*(2^n)-space algorithm for DISTORTION by Fomin et al. to an algorithm working in O(4.383^n) time and space.


Formal Proof of SCHUR Conjugate Function

Abstract: The main goal of our work is to formally prove the correctness of the key commands of the SCHUR software, an interactive program for calculating with characters of Lie groups and symmetric functions. The core of the computations relies on enumeration and manipulation of combinatorial structures. As a first "proof of concept", we present a formal proof of the conjugate function, written in C. This function computes the conjugate of an integer partition. To formally prove this program, we use the Frama-C software. It allows us to annotate C functions and to generate proof obligations, which are proved using several automated theorem provers. In this paper, we also draw on methodology, discussing on how to formally prove this kind of program.


Dimensions of Formality: A Case Study for MKM in Software Engineering

Abstract: We study the formalization of a collection of documents created for a Software Engineering project from an MKM perspective. We analyze how document and collection markup formats can cope with an open-ended, multi-dimensional space of primary and secondary classifications and relationships. We show that RDFa-based extensions of MKM formats, employing flexible "metadata" relationships referencing specific vocabularies for distinct dimensions, are well-suited to encode this and to put it into service. This formalized knowledge can be used for enriching interactive document browsing, for enabling multi-dimensional metadata queries over documents and collections, and for exporting Linked Data to the Semantic Web and thus enabling further reuse.


Space Complexity of Perfect Matching in Bounded Genus Bipartite Graphs

Abstract: We investigate the space complexity of certain perfect matching problems over bipartite graphs embedded on surfaces of constant genus (orientable or non-orientable). We show that the problems of deciding whether such graphs have (1) a perfect matching or not and (2) a unique perfect matching or not, are in the logspace complexity class \SPL. Since \SPL\ is contained in the logspace counting classes $\oplus\L$ (in fact in \modk\ for all $k\geq 2$), \CeqL, and \PL, our upper bound places the above-mentioned matching problems in these counting classes as well. We also show that the search version, computing a perfect matching, for this class of graphs is in $\FL^{\SPL}$. Our results extend the same upper bounds for these problems over bipartite planar graphs known earlier. As our main technical result, we design a logspace computable and polynomially bounded weight function which isolates a minimum weight perfect matching in bipartite graphs embedded on surfaces of constant genus. We use results from algebraic topology for proving the correctness of the weight function.


Epistemic Model Checking for Knowledge-Based Program Implementation: an Application to Anonymous Broadcast

Abstract: Knowledge-based programs provide an abstract level of description of protocols in which agent actions are related to their states of knowledge. The paper describes how epistemic model checking technology may be applied to discover and verify concrete implementations based on this abstract level of description. The details of the implementations depend on the specific context of use of the protocol. The knowledge-based approach enables the implementations to be optimized relative to these conditions of use. The approach is illustrated using extensions of the Dining Cryptographers protocol, a security protocol for anonymous broadcast.


Notations Around the World: Census and Exploitation

Abstract: Mathematical notations around the world are diverse. Not as much as requiring computing machines' makers to adapt to each culture, but as much as to disorient a person landing on a web-page with a text in mathematics. In order to understand better this diversity, we are building a census of notations: it should allow any content creator or mathematician to grasp which mathematical notation is used in which language and culture. The census is built collaboratively, collected in pages with a given semantic and presenting observations of the widespread notations being used in existing materials by a graphical extract. We contend that our approach should dissipate the fallacies found here and there about the notations in "other cultures" so that a better understanding of the cultures can be realized. The exploitation of the census in the math-bridge project is also presented: this project aims at taking learners "where they are in their math-knowledge" and bring them to a level ready to start engineering studies. The census serves as definitive reference for the transformation elements that generate the rendering of formul{\ae} in web-browsers.


Efficient and Effective Spam Filtering and Re-ranking for Large Web Datasets

Abstract: The TREC 2009 web ad hoc and relevance feedback tasks used a new document collection, the ClueWeb09 dataset, which was crawled from the general Web in early 2009. This dataset contains 1 billion web pages, a substantial fraction of which are spam --- pages designed to deceive search engines so as to deliver an unwanted payload. We examine the effect of spam on the results of the TREC 2009 web ad hoc and relevance feedback tasks, which used the ClueWeb09 dataset. We show that a simple content-based classifier with minimal training is efficient enough to rank the "spamminess" of every page in the dataset using a standard personal computer in 48 hours, and effective enough to yield significant and substantive improvements in the fixed-cutoff precision (estP10) as well as rank measures (estR-Precision, StatMAP, MAP) of nearly all submitted runs. Moreover, using a set of "honeypot" queries the labeling of training data may be reduced to an entirely automatic process. The results of classical information retrieval methods are particularly enhanced by filtering --- from among the worst to among the best.


Multiscale approach for the network compression-friendly ordering

Abstract: We present a fast multiscale approach for the network minimum logarithmic arrangement problem. This type of arrangement plays an important role in a network compression and fast node/link access operations. The algorithm is of linear complexity and exhibits good scalability which makes it practical and attractive for using on large-scale instances. Its effectiveness is demonstrated on a large set of real-life networks. These networks with corresponding best-known minimization results are suggested as an open benchmark for a research community to evaluate new methods for this problem.


The Application of a Dendritic Cell Algorithm to a Robotic Classifier

Abstract: The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested.


Circuits with arbitrary gates for random operators

Abstract: We consider boolean circuits computing n-operators f:{0,1}^n --> {0,1}^n. As gates we allow arbitrary boolean functions; neither fanin nor fanout of gates is restricted. An operator is linear if it computes n linear forms, that is, computes a matrix-vector product y=Ax over GF(2). We prove the existence of n-operators requiring about n^2 wires in any circuit, and linear n-operators requiring about n^2/\log n wires in depth-2 circuits, if either all output gates or all gates on the middle layer are linear.


Construction auto-stabilisante d'arbre couvrant en d\'epit d'actions malicieuses

Abstract: A self-stabilizing protocol provides by definition a tolerance to transient failures. Recently, a new class of self-stabilizing protocols appears. These protocols provides also a tolerance to a given number of permanent failures. In this article, we are interested in self-stabilizing protocols that deal with Byzantines failures. We prove that, for some problems which not allow strict stabilization (see [Nesterenko,Arora,2002]), there exist solutions that tolerates Byzantine faults if we define a new criteria of tolerance.


On the Rationality of Escalation

Abstract: Escalation is a typical feature of infinite games. Therefore tools conceived for studying infinite mathematical structures, namely those deriving from coinduction are essential. Here we use coinduction, or backward coinduction (to show its connection with the same concept for finite games) to study carefully and formally the infinite games especially those called dollar auctions, which are considered as the paradigm of escalation. Unlike what is commonly admitted, we show that, provided one assumes that the other agent will always stop, bidding is rational, because it results in a subgame perfect equilibrium. We show that this is not the only rational strategy profile (the only subgame perfect equilibrium). Indeed if an agent stops and will stop at every step, we claim that he is rational as well, if one admits that his opponent will never stop, because this corresponds to a subgame perfect equilibrium. Amazingly, in the infinite dollar auction game, the behavior in which both agents stop at each step is not a Nash equilibrium, hence is not a subgame perfect equilibrium, hence is not rational.


Nearly Optimal Algorithms for the Decomposition of Multivariate Rational Functions and the Extended L\"uroth's Theorem

Abstract: The extended L\"uroth's Theorem says that if the transcendence degree of $\KK(\mathsf{f}_1,\dots,\mathsf{f}_m)/\KK$ is 1 then there exists $f \in \KK(\underline{X})$ such that $\KK(\mathsf{f}_1,\dots,\mathsf{f}_m)$ is equal to $\KK(f)$. In this paper we show how to compute $f$ with a probabilistic algorithm. We also describe a probabilistic and a deterministic algorithm for the decomposition of multivariate rational functions. The probabilistic algorithms proposed in this paper are softly optimal when $n$ is fixed and $d$ tends to infinity. We also give an indecomposability test based on gcd computations and Newton's polytope. In the last section, we show that we get a polynomial time algorithm, with a minor modification in the exponential time decomposition algorithm proposed by Gutierez-Rubio-Sevilla in 2001.


A characterization of b-perfect graphs

Abstract: A b-coloring is a coloring of the vertices of a graph such that each color class contains a vertex that has a neighbor in all other color classes, and the b-chromatic number of a graph $G$ is the largest integer $k$ such that $G$ admits a b-coloring with $k$ colors. A graph is b-perfect if the b-chromatic number is equal to the chromatic number for every induced subgraph of $G$. We prove that a graph is b-perfect if and only if it does not contain as an induced subgraph a member of a certain list of twenty-two graphs. This entails the existence of a polynomial-time recognition algorithm and of a polynomial-time algorithm for coloring exactly the vertices of every b-perfect graph.


Settling the complexity of local max-cut (almost) completely

Abstract: We consider the problem of finding a local optimum for Max-Cut with FLIP-neighborhood, in which exactly one node changes the partition. Schaeffer and Yannakakis (SICOMP, 1991) showed PLS-completeness of this problem on graphs with unbounded degree. On the other side, Poljak (SICOMP, 1995) showed that in cubic graphs every FLIP local search takes O(n^2) steps, where n is the number of nodes. Due to the huge gap between degree three and unbounded degree, Ackermann, Roeglin, and Voecking (JACM, 2008) asked for the smallest d for which the local Max-Cut problem with FLIP-neighborhood on graphs with maximum degree d is PLS-complete. In this paper, we prove that the computation of a local optimum on graphs with maximum degree five is PLS-complete. Thus, we solve the problem posed by Ackermann et al. almost completely by showing that d is either four or five (unless PLS is in P). On the other side, we also prove that on graphs with degree O(log n) every FLIP local search has probably polynomial smoothed complexity. Roughly speaking, for any instance, in which the edge weights are perturbated by a (Gaussian) random noise with variance \sigma^2, every FLIP local search terminates in time polynomial in n and \sigma^{-1}, with probability 1-n^{-\Omega(1)}. Putting both results together, we may conclude that although local Max-Cut is likely to be hard on graphs with bounded degree, it can be solved in polynomial time for slightly perturbated instances with high probability.


Query strategy for sequential ontology debugging

Abstract: Debugging of ontologies is an important prerequisite for their wide-spread application, especially in areas that rely upon everyday users to create and maintain knowledge bases, as in the case of the Semantic Web. Recent approaches use diagnosis methods to identify causes of inconsistent or incoherent ontologies. However, in most debugging scenarios these methods return many alternative diagnoses, thus placing the burden of fault localization on the user. This paper demonstrates how the target diagnosis can be identified by performing a sequence of observations, that is, by querying an oracle about entailments of the target ontology. We exploit a-priori probabilities of typical user errors to formulate information-theoretic concepts for query selection. Our evaluation showed that the proposed method significantly reduces the number of required queries compared to myopic strategies. We experimented with different probability distributions of user errors and different qualities of the a-priori probabilities. Our measurements showed the advantageousness of information-theoretic approach to query selection even in cases where only a rough estimate of the priors is available.


Using a Model of Social Dynamics to Predict Popularity of News

Abstract: Popularity of content in social media is unequally distributed, with some items receiving a disproportionate share of attention from users. Predicting which newly-submitted items will become popular is critically important for both companies that host social media sites and their users. Accurate and timely prediction would enable the companies to maximize revenue through differential pricing for access to content or ad placement. Prediction would also give consumers an important tool for filtering the ever-growing amount of content. Predicting popularity of content in social media, however, is challenging due to the complex interactions among content quality, how the social media site chooses to highlight content, and influence among users. While these factors make it difficult to predict popularity \emph{a priori}, we show that stochastic models of user behavior on these sites allows predicting popularity based on early user reactions to new content. By incorporating aspects of the web site design, such models improve on predictions based on simply extrapolating from the early votes. We validate this claim on the social news portal Digg using a previously-developed model of social voting based on the Digg user interface.


Self-Taught Hashing for Fast Similarity Search

Abstract: The ability of fast similarity search at large scale is of great importance to many Information Retrieval (IR) applications. A promising way to accelerate similarity search is semantic hashing which designs compact binary codes for a large number of documents so that semantically similar documents are mapped to similar codes (within a short Hamming distance). Although some recently proposed techniques are able to generate high-quality codes for documents known in advance, obtaining the codes for previously unseen documents remains to be a very challenging problem. In this paper, we emphasise this issue and propose a novel Self-Taught Hashing (STH) approach to semantic hashing: we first find the optimal $l$-bit binary codes for all documents in the given corpus via unsupervised learning, and then train $l$ classifiers via supervised learning to predict the $l$-bit code for any query document unseen before. Our experiments on three real-world text datasets show that the proposed approach using binarised Laplacian Eigenmap (LapEig) and linear Support Vector Machine (SVM) outperforms state-of-the-art techniques significantly.


Interface Building for Software by Modular Three-Valued Abstraction Refinement

Abstract: Verification of software systems is a very hard problem due to the large size of program state-space. The traditional techniques (like model checking) do not scale; since they include the whole state-space by inlining the library function codes. Current research avoids these problem by creating a lightweight representation of the library in form of an "interface graph" (call sequence graph). In this paper we introduce a new algorithm to compute a safe, permissive interface graph for C-type functions. In this modular analysis, each function transition is summarized following three-valued abstraction semantics. There are two kinds of abstraction used here. The global abstraction contains predicates over global variables only; however the local abstraction inside each function may also contain the local variables. The abstract summary needs refinement to guarantee safety and permissiveness. We have implemented the algorithms in TICC tool and compared this algorithm with some related interface generation algorithms. We also discuss the application of interface as an offline test-suite. We create an interface from the model program (specification) and the interface will act as a test-suite for the new implementation-under-test (IUT).


Graphic Symbol Recognition using Graph Based Signature and Bayesian Network Classifier

Abstract: We present a new approach for recognition of complex graphic symbols in technical documents. Graphic symbol recognition is a well known challenge in the field of document image analysis and is at heart of most graphic recognition systems. Our method uses structural approach for symbol representation and statistical classifier for symbol recognition. In our system we represent symbols by their graph based signatures: a graphic symbol is vectorized and is converted to an attributed relational graph, which is used for computing a feature vector for the symbol. This signature corresponds to geometry and topology of the symbol. We learn a Bayesian network to encode joint probability distribution of symbol signatures and use it in a supervised learning scenario for graphic symbol recognition. We have evaluated our method on synthetically deformed and degraded images of pre-segmented 2D architectural and electronic symbols from GREC databases and have obtained encouraging recognition rates.


Employing fuzzy intervals and loop-based methodology for designing structural signature: an application to symbol recognition

Abstract: Motivation of our work is to present a new methodology for symbol recognition. We support structural methods for representing visual associations in graphic documents. The proposed method employs a structural approach for symbol representation and a statistical classifier for recognition. We vectorize a graphic symbol, encode its topological and geometrical information by an ARG and compute a signature from this structural graph. To address the sensitivity of structural representations to deformations and degradations, we use data adapted fuzzy intervals while computing structural signature. The joint probability distribution of signatures is encoded by a Bayesian network. This network in fact serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures, for underlying symbol set. Finally we deploy the Bayesian network in supervised learning scenario for recognizing query symbols. We have evaluated the robustness of our method against noise, on synthetically deformed and degraded images of pre-segmented 2D architectural and electronic symbols from GREC databases and have obtained encouraging recognition rates. A second set of experimentation was carried out for evaluating the performance of our method against context noise i.e. symbols cropped from complete documents. The results support the use of our signature by a symbol spotting system.


Some Results on the Functional Decomposition of Polynomials

Abstract: If g and h are functions over some field, we can consider their composition f = g(h). The inverse problem is decomposition: given f, determine the ex- istence of such functions g and h. In this thesis we consider functional decom- positions of univariate and multivariate polynomials, and rational functions over a field F of characteristic p. In the polynomial case, "wild" behaviour occurs in both the mathematical and computational theory of the problem if p divides the degree of g. We consider the wild case in some depth, and deal with those polynomials whose decompositions are in some sense the "wildest": the additive polynomials. We determine the maximum number of decompositions and show some polynomial time algorithms for certain classes of polynomials with wild decompositions. For the rational function case we present a definition of the problem, a normalised version of the problem to which the general problem reduces, and an exponential time solution to the normal problem.


The Role of Boolean Function in Fractal Formation and it s Application to CDMA Wireless Communication

Abstract: In this paper, a new transformation is generated from a three variable Boolean function 3, which is used to produce a self-similar fractal pattern of dimension 1.58. This very fractal pattern is used to reconstruct the whole structural position of resources in wireless CDMA network. This reconstruction minimizes the number of resources in the network and so naturally network consumption costs are getting reduced. Now -a -days resource controlling and cost minimization are still a severe problem in wireless CDMA network. To overcome this problem fractal pattern produced in our research provides a complete solution of structural position of resources in this Wireless CDMA Network.


Simple Type Theory as Framework for Combining Logics

Abstract: Simple type theory is suited as framework for combining classical and non-classical logics. This claim is based on the observation that various prominent logics, including (quantified) multimodal logics and intuitionistic logics, can be elegantly embedded in simple type theory. Furthermore, simple type theory is sufficiently expressive to model combinations of embedded logics and it has a well understood semantics. Off-the-shelf reasoning systems for simple type theory exist that can be uniformly employed for reasoning within and about combinations of logics.


Security Estimates for Quadratic Field Based Cryptosystems

Abstract: We describe implementations for solving the discrete logarithm problem in the class group of an imaginary quadratic field and in the infrastructure of a real quadratic field. The algorithms used incorporate improvements over previously-used algorithms, and extensive numerical results are presented demonstrating their efficiency. This data is used as the basis for extrapolations, used to provide recommendations for parameter sizes providing approximately the same level of security as block ciphers with $80,$ $112,$ $128,$ $192,$ and $256$-bit symmetric keys.


Minimum Manhattan network problem in normed planes with polygonal balls: a factor 2.5 approximation algorithm

Abstract: Let B be a centrally symmetric convex polygon of R^2 and || p - q || be the distance between two points p,q in R^2 in the normed plane whose unit ball is B. For a set T of n points (terminals) in R^2, a B-Manhattan network on T is a network N(T) = (V,E) with the property that its edges are parallel to the directions of B and for every pair of terminals t_i and t_j, the network N(T) contains a shortest B-path between them, i.e., a path of length || t_i - t_j ||. A minimum B-Manhattan network on T is a B-Manhattan network of minimum possible length. The problem of finding minimum B-Manhattan networks has been introduced by Gudmundsson, Levcopoulos, and Narasimhan (APPROX'99) in the case when the unit ball B is a square (and hence the distance || p - q || is the l_1 or the l_infty-distance between p and q) and it has been shown recently by Chin, Guo, and Sun (SoCG'09) to be strongly NP-complete. Several approximation algorithms (with factors 8, 4 ,3 , and 2) for minimum Manhattan problem are known. In this paper, we propose a factor 2.5 approximation algorithm for minimum B-Manhattan network problem. The algorithm employs a simplified version of the strip-staircase decomposition proposed in our paper (APPROX'05) and subsequently used in other factor 2 approximation algorithms for minimum Manhattan problem.


Fully Countering Trusting Trust through Diverse Double-Compiling

Abstract: An Air Force evaluation of Multics, and Ken Thompson's Turing award lecture ("Reflections on Trusting Trust"), showed that compilers can be subverted to insert malicious Trojan horses into critical software, including themselves. If this "trusting trust" attack goes undetected, even complete analysis of a system's source code will not find the malicious code that is running. Previously-known countermeasures have been grossly inadequate. If this attack cannot be countered, attackers can quietly subvert entire classes of computer systems, gaining complete control over financial, infrastructure, military, and/or business systems worldwide. This dissertation's thesis is that the trusting trust attack can be detected and effectively countered using the "Diverse Double-Compiling" (DDC) technique, as demonstrated by (1) a formal proof that DDC can determine if source code and generated executable code correspond, (2) a demonstration of DDC with four compilers (a small C compiler, a small Lisp compiler, a small maliciously corrupted Lisp compiler, and a large industrial-strength C compiler, GCC), and (3) a description of approaches for applying DDC in various real-world scenarios. In the DDC technique, source code is compiled twice: the source code of the compiler's parent is compiled using a trusted compiler, and then the putative compiler source code is compiled using the result of the first compilation. If the DDC result is bit-for-bit identical with the original compiler-under-test's executable, and certain other assumptions hold, then the compiler-under-test's executable corresponds with its putative source code.


Countering Trusting Trust through Diverse Double-Compiling

Abstract: An Air Force evaluation of Multics, and Ken Thompson's famous Turing award lecture "Reflections on Trusting Trust," showed that compilers can be subverted to insert malicious Trojan horses into critical software, including themselves. If this attack goes undetected, even complete analysis of a system's source code will not find the malicious code that is running, and methods for detecting this particular attack are not widely known. This paper describes a practical technique, termed diverse double-compiling (DDC), that detects this attack and some compiler defects as well. Simply recompile the source code twice: once with a second (trusted) compiler, and again using the result of the first compilation. If the result is bit-for-bit identical with the untrusted binary, then the source code accurately represents the binary. This technique has been mentioned informally, but its issues and ramifications have not been identified or discussed in a peer-reviewed work, nor has a public demonstration been made. This paper describes the technique, justifies it, describes how to overcome practical challenges, and demonstrates it.


On the (Im)possibility of Preserving Utility and Privacy in Personalized Social Recommendations

Abstract: With the recent surge of social networks like Facebook, new forms of recommendations have become possible -- personalized recommendations of ads, content, and even new social and product connections based on one's social interactions. In this paper, we study whether "social recommendations", or recommendations that utilize a user's social network, can be made without disclosing sensitive links between users. More precisely, we quantify the loss in utility when existing recommendation algorithms are modified to satisfy a strong notion of privacy called differential privacy. We propose lower bounds on the minimum loss in utility for any recommendation algorithm that is differentially private. We also propose two recommendation algorithms that satisfy differential privacy, analyze their performance in comparison to the lower bound, both analytically and experimentally, and show that good private social recommendations are feasible only for a few users in the social network or for a lenient setting of privacy parameters.


Learning from Multiple Outlooks

Abstract: We propose a novel problem formulation of learning a single task when the data are provided in different feature spaces. Each such space is called an outlook, and is assumed to contain both labeled and unlabeled data. The objective is to take advantage of the data from all the outlooks to better classify each of the outlooks. We devise an algorithm that computes optimal affine mappings from different outlooks to a target outlook by matching moments of the empirical distributions. We further derive a probabilistic interpretation of the resulting algorithm and a sample complexity bound indicating how many samples are needed to adequately find the mapping. We report the results of extensive experiments on activity recognition tasks that show the value of the proposed approach in boosting performance.


A Framework For Fully-Simulatable $h$-Out-Of-$n$ Oblivious Transfer

Abstract: We present a framework for fully-simulatable $h$-out-of-$n$ oblivious transfer ($OT^{n}_{h}$) with security against non-adaptive malicious adversaries. The framework costs six communication rounds and costs at most $40n$ public-key operations in computational overhead. Compared with the known protocols for fully-simulatable oblivious transfer that works in the plain mode (where there is no trusted common reference string available) and proven to be secure under standard model (where there is no random oracle available), the instantiation based on the decisional Diffie-Hellman assumption of the framework is the most efficient one, no matter seen from communication rounds or computational overhead. Our framework uses three abstract tools, i.e., perfectly binding commitment, perfectly hiding commitment and our new smooth projective hash. This allows a simple and intuitive understanding of its security. We instantiate the new smooth projective hash under the lattice assumption, the decisional Diffie-Hellman assumption, the decisional $N$-th residuosity assumption, the decisional quadratic residuosity assumption. This indeed shows that the folklore that it is technically difficult to instantiate the projective hash framework under the lattice assumption is not true. What's more, by using this lattice-based hash and lattice-based commitment scheme, we gain a concrete protocol for $OT^{n}_{h}$ which is secure against quantum algorithms.


A Geometric View of Conjugate Priors

Abstract: In Bayesian machine learning, conjugate priors are popular, mostly due to mathematical convenience. In this paper, we show that there are deeper reasons for choosing a conjugate prior. Specifically, we formulate the conjugate prior in the form of Bregman divergence and show that it is the inherent geometry of conjugate priors that makes them appropriate and intuitive. This geometric interpretation allows one to view the hyperparameters of conjugate priors as the {\it effective} sample points, thus providing additional intuition. We use this geometric understanding of conjugate priors to derive the hyperparameters and expression of the prior used to couple the generative and discriminative components of a hybrid model for semi-supervised learning.


Global Linear Complexity Analysis of Filter Keystream Generators

Abstract: An efficient algorithm for computing lower bounds on the global linear complexity of nonlinearly filtered PN-sequences is presented. The technique here developed is based exclusively on the realization of bit wise logic operations, which makes it appropriate for both software simulation and hardware implementation. The present algorithm can be applied to any arbitrary nonlinear function with a unique term of maximum order. Thus, the extent of its application for different types of filter generators is quite broad. Furthermore, emphasis is on the large lower bounds obtained that confirm the exponential growth of the global linear complexity for the class of nonlinearly filtered sequences.


Secret Sharing Based on a Hard-on-Average Problem

Abstract: The main goal of this work is to propose the design of secret sharing schemes based on hard-on-average problems. It includes the description of a new multiparty protocol whose main application is key management in networks. Its unconditionally perfect security relies on a discrete mathematics problem classiffied as DistNP-Complete under the average-case analysis, the so-called Distributional Matrix Representability Problem. Thanks to the use of the search version of the mentioned decision problem, the security of the proposed scheme is guaranteed. Although several secret sharing schemes connected with combinatorial structures may be found in the bibliography, the main contribution of this work is the proposal of a new secret sharing scheme based on a hard-on-average problem, which allows to enlarge the set of tools for designing more secure cryptographic applications.


On the Design of Cryptographic Primitives

Abstract: The main objective of this work is twofold. On the one hand, it gives a brief overview of the area of two-party cryptographic protocols. On the other hand, it proposes new schemes and guidelines for improving the practice of robust protocol design. In order to achieve such a double goal, a tour through the descriptions of the two main cryptographic primitives is carried out. Within this survey, some of the most representative algorithms based on the Theory of Finite Fields are provided and new general schemes and specific algorithms based on Graph Theory are proposed.


Linear solutions for cryptographic nonlinear sequence generators

Abstract: This letter shows that linear Cellular Automata based on rules 90/150 generate all the solutions of linear difference equations with binary constant coefficients. Some of these solutions are pseudo-random noise sequences with application in cryptography: the sequences generated by the class of shrinking generators. Consequently, this contribution show that shrinking generators do not provide enough guarantees to be used for encryption purposes. Furthermore, the linearization is achieved through a simple algorithm about which a full description is provided.


Distributive Stochastic Learning for Delay-Optimal OFDMA Power and Subband Allocation

Abstract: In this paper, we consider the distributive queue-aware power and subband allocation design for a delay-optimal OFDMA uplink system with one base station, $K$ users and $N_F$ independent subbands. Each mobile has an uplink queue with heterogeneous packet arrivals and delay requirements. We model the problem as an infinite horizon average reward Markov Decision Problem (MDP) where the control actions are functions of the instantaneous Channel State Information (CSI) as well as the joint Queue State Information (QSI). To address the distributive requirement and the issue of exponential memory requirement and computational complexity, we approximate the subband allocation Q-factor by the sum of the per-user subband allocation Q-factor and derive a distributive online stochastic learning algorithm to estimate the per-user Q-factor and the Lagrange multipliers (LM) simultaneously and determine the control actions using an auction mechanism. We show that under the proposed auction mechanism, the distributive online learning converges almost surely (with probability 1). For illustration, we apply the proposed distributive stochastic learning framework to an application example with exponential packet size distribution. We show that the delay-optimal power control has the {\em multi-level water-filling} structure where the CSI determines the instantaneous power allocation and the QSI determines the water-level. The proposed algorithm has linear signaling overhead and computational complexity $\mathcal O(KN)$, which is desirable from an implementation perspective.


Electronic Geometry Textbook: A Geometric Textbook Knowledge Management System

Abstract: Electronic Geometry Textbook is a knowledge management system that manages geometric textbook knowledge to enable users to construct and share dynamic geometry textbooks interactively and efficiently. Based on a knowledge base organizing and storing the knowledge represented in specific languages, the system implements interfaces for maintaining the data representing that knowledge as well as relations among those data, for automatically generating readable documents for viewing or printing, and for automatically discovering the relations among knowledge data. An interface has been developed for users to create geometry textbooks with automatic checking, in real time, of the consistency of the structure of each resulting textbook. By integrating an external geometric theorem prover and an external dynamic geometry software package, the system offers the facilities for automatically proving theorems and generating dynamic figures in the created textbooks. This paper provides a comprehensive account of the current version of Electronic Geometry Textbook.


A Rational Approach to Cryptographic Protocols

Abstract: This work initiates an analysis of several cryptographic protocols from a rational point of view using a game-theoretical approach, which allows us to represent not only the protocols but also possible misbehaviours of parties. Concretely, several concepts of two-person games and of two-party cryptographic protocols are here combined in order to model the latters as the formers. One of the main advantages of analysing a cryptographic protocol in the game-theory setting is the possibility of describing improved and stronger cryptographic solutions because possible adversarial behaviours may be taken into account directly. With those tools, protocols can be studied in a malicious model in order to find equilibrium conditions that make possible to protect honest parties against all possible strategies of adversaries.


Using Smith Normal Forms and mu-Bases to Compute all the Singularities of Rational Planar Curves

Abstract: We prove the conjecture of Chen, Wang and Liu in [8] concerning how to calculate the parameter values corresponding to all the singu- larities, including the infinitely near singularities, of rational planar curves from the Smith normal forms of certain Bezout resultant ma- trices derived from mu-bases.


Linear Cellular Automata as Discrete Models for Generating Cryptographic Sequences

Abstract: In this paper, we develop a new cellular automata-based linear model for several nonlinear pseudorandom number generators with practical applications in symmetric cryptography. Such a model generates all the solutions of linear binary difference equations as well as many of these solutions are pseudo-random keystream sequences. In this way, a linear structure based on cellular automata may be used to generate not only difference equation solutions but also cryptographic sequences. The proposed model is very simple since it is based exclusively on successive concatenations of a basic linear automaton.


New Attack Strategy for the Shrinking Generator

Abstract: This work shows that the cryptanalysis of the shrinking generator requires fewer intercepted bits than what indicated by the linear complexity. Indeed, whereas the linear complexity of shrunken sequences is between $A \cdot 2^(S-2)$ and $A \cdot 2^(S-1)$, we claim that the initial states of both component registers are easily computed with less than $A \cdot S$ shrunken bits. Such a result is proven thanks to the definition of shrunken sequences as interleaved sequences. Consequently, it is conjectured that this statement can be extended to all interleaved sequences. Furthermore, this paper confirms that certain bits of the interleaved sequences have a greater strategic importance than others, which may be considered as a proof of weakness of interleaved generators.


The Exact Closest String Problem as a Constraint Satisfaction Problem

Abstract: We report (to our knowledge) the first evaluation of Constraint Satisfaction as a computational framework for solving closest string problems. We show that careful consideration of symbol occurrences can provide search heuristics that provide several orders of magnitude speedup at and above the optimal distance. We also report (to our knowledge) the first analysis and evaluation -- using any technique -- of the computational difficulties involved in the identification of all closest strings for a given input set. We describe algorithms for web-scale distributed solution of closest string problems, both purely based on AI backtrack search and also hybrid numeric-AI methods.


Influence of distortions of key frames on video transfer in wireless networks

Abstract: In this paper it is shown that for substantial increase of video quality in wireless network it is necessary to execute two obligatory points on modernization of the communication scheme. The player on the received part should throw back automatically duplicated RTP packets, server of streaming video should duplicate the packets containing the information of key frames. Coefficients of the mathematical model describing video quality in wireless network have been found for WiFi and 3G standards and codecs MPEG-2 and MPEG-4 (DivX). The special experimental technique which has allowed collecting and processing the data has been developed for calculation of values of factors.


A Simple Attack on Some Clock-Controlled Generators

Abstract: We present a new approach to edit distance attacks on certain clock-controlled generators, which applies basic concepts of Graph Theory to simplify the search trees of the original attacks in such a way that only the most promising branches are analyzed. In particular, the proposed improvement is based on cut sets defined on some graphs so that certain shortest paths provide the edit distances. The strongest aspects of the proposal are that the obtained results from the attack are absolutely deterministic, and that many inconsistent initial states of the target registers are recognized beforehand and avoided during search.


Joint Structured Models for Extraction from Overlapping Sources

Abstract: We consider the problem of jointly training structured models for extraction from sources whose instances enjoy partial overlap. This has important applications like user-driven ad-hoc information extraction on the web. Such applications present new challenges in terms of the number of sources and their arbitrary pattern of overlap not seen by earlier collective training schemes applied on two sources. We present an agreement-based learning framework and alternatives within it to trade-off tractability, robustness to noise, and extent of agreement. We provide a principled scheme to discover low-noise agreement sets in unlabeled data across the sources. Through extensive experiments over 58 real datasets, we establish that our method of additively rewarding agreement over maximal segments of text provides the best trade-offs, and also scores over alternatives such as collective inference, staged training, and multi-view learning.


Self-Organized Authentication in Mobile Ad-hoc Networks

Abstract: This work proposes a new distributed and self-organized authentication scheme for Mobile Ad-hoc NETworks (MANETs). Apart from describing all its components, special emphasis is placed on proving that the proposal fulfils most requirements derived from the special characteristics of MANETs, including limited physical protection of broadcast medium, frequent route changes caused by mobility, and lack of structured hierarchy. Interesting conclusions are obtained from an analysis of simulation experiments in different scenarios.


Using Linear Difference Equations to Model Nonlinear Cryptographic Sequences

Abstract: A new class of linear sequence generators based on cellular automata is here introduced in order to model several nonlinear keystream generators with practical applications in symmetric cryptography. The output sequences are written as solutions of linear difference equations, and three basic properties (period, linear complexity and number of different output sequences) are analyzed.


Security Scheme for Distributed DoS in Mobile Ad Hoc Networks

Abstract: In Mobile Ad Hoc Networks (MANET), various types of Denial of Service Attacks (DoS) are possible because of the inherent limitations of its routing protocols. Considering the Ad Hoc On Demand Vector (AODV) routing protocol as the base protocol it is possible to find a suitable solution to over-come the attack of initiating / forwarding fake Route Requests (RREQs) that lead to hogging of network resources and hence denial of service to genuine nodes. In this paper, a proactive scheme is proposed that could prevent a specific kind of DoS attack and identify the misbehaving node. Since the proposed scheme is distributed in nature it has the capability to prevent Distributed DoS (DDoS) as well. The performance of the proposed algorithm in a series of simulations reveal that the proposed scheme provides a better solution than existing approaches with no extra overhead.


Adaptive Bases for Reinforcement Learning

Abstract: We consider the problem of reinforcement learning using function approximation, where the approximating basis can change dynamically while interacting with the environment. A motivation for such an approach is maximizing the value function fitness to the problem faced. Three errors are considered: approximation square error, Bellman residual, and projected Bellman residual. Algorithms under the actor-critic framework are presented, and shown to converge. The advantage of such an adaptive basis is demonstrated in simulations.


Slowly synchronizing automata and digraphs

Abstract: We present several infinite series of synchronizing automata for which the minimum length of reset words is close to the square of the number of states. These automata are closely related to primitive digraphs with large exponent.


ACRR: Ad Hoc On-Demand Distance Vector Routing with Controlled Route Requests

Abstract: Reactive routing protocols like Ad Hoc On-Demand Distance Vector Routing (AODV) and Dynamic Source Routing (DSR)in Ad-Hoc Wireless Networks which are used in Mobile and Ad Hoc Networks (MANETs) work by flooding the network with control packets. There is generally a limit on the number of these packets that can be generated or forwarded. But a malicious node can disregard this limit and flood the network with fake control packets. These packets hog the limited bandwidth and processing power of genuine nodes in the network while being forwarded. Due to this, genuine route requests suffer and many routes either do not get a chance to materialize or they end up being longer than otherwise. In this paper we propose a non cryptographic solution to the above problem and prove its efficiency by means of simulation.


The Formulator MathML Editor Project: User-Friendly Authoring of Content Markup Documents

Abstract: Implementation of an editing process for Content MathML formulas in common visual style is a real challenge for a software developer who does not really want the user to have to understand the structure of Content MathML in order to edit an expression, since it is expected that users are often not that technically minded. In this paper, we demonstrate how this aim is achieved in the context of the Formulator project and discuss features of this MathML editor, which provides a user with a WYSIWYG editing style while authoring MathML documents with Content or mixed markup. We also present the approach taken to enhance availability of the MathML editor to end-users, demonstrating an online version of the editor that runs inside a Web browser.


Software Requirements Specification of the IUfA's UUIS -- a Team 4 COMP5541-W10 Project Approach

Abstract: This document presents the business requirement of Unified University Inventory System (UUIS) in Technology-independent manner. All attempts have been made in using mostly business terminology and business language while describing the requirements in this document. Very minimal and commonly understood Technical terminology is used. Use case approach is used in modeling the business requirements in this document.


Software Design Document, Testing, Deployment and Configuration Management, and User Manual of the UUIS -- a Team 4 COMP5541-W10 Project Approach

Abstract: This document provides a description of the technical design for Unified University Inventory System - Web Portal. This document's primary purpose is to describe the technical vision for how business requirements will be realized. This document provides an architectural overview of the system to depict different aspects of the system. This document also functions as a foundational reference point for developers.


Analysis of Non-Persistent CSMA Protocols with Exponential Backoff Scheduling

Abstract: This paper studies the performance of Non-persistent CSMA/CA protocols with K-Exponential Backoff scheduling algorithms. A multi-queue single-server system is proposed to model multiple access networks. The input buffer of each access node is modeled as a Geo/G/1 queue, and the service time distribution of head-of-line packets is derived from the Markov chain of underlying scheduling algorithm. The main results include the complete analysis of the throughput and delay distribution, from which we obtained stable regions with respect to the throughput and bounded mean delay of the Geometric Retransmission and Exponential Backoff schemes. We show that the throughput stable region of Geometric Retransmission will vanish as the number of nodes n \rightarrow \infty; thus, it is inherently unstable for large n. In contrast to Geometric Retransmission, the throughput stable region of Exponential Backoff can be obtained for an infinite population. We found that the bounded mean delay region of Geometric Retransmission remains the same as its throughput stable region. Besides, the variance of service time of Exponential Backoff can be unbounded due to the capture effect; thus, its bounded delay region is only a sub-set of its throughput stable region. Analytical results presented in this paper are all verified by simulation.


Personnalisation de Syst\`emes OLAP Annot\'es

Abstract: This paper deals with personalization of annotated OLAP systems. Data constellation is extended to support annotations and user preferences. Annotations reflect the decision-maker experience whereas user preferences enable users to focus on the most interesting data. User preferences allow annotated contextual recommendations helping the decision-maker during his/her multidimensional navigations.


Personnalisation de bases de donn\'ees multidimensionnelles

Abstract: This paper deals with decision support systems resting on multidimensional modelling of data. Moreover, we intend to offer a set of concepts and mechanisms for personalized multidimensional database specifications. This personalization consists in associating weights to different components of a multidimensional schema. Personalization specifications are specified through the use of a language based on the principle of Event Condition Action. This personalisation determines multidimensional data display as well as their analyses (with the use of drilling or rotating operations).


Construction graphique d'entrep\^ots et de magasins de donn\'ees

Abstract: Nowadays, decisional systems have became a significant research topic in databases. Data warehouses and data marts are the main elements of such systems. This paper presents our decisional support system. We present graphical interfaces which help the administrator to build data warehouses and data marts. We present a data warehouse building interface based on an object-oriented conceptual model. This model allows the warehouse data historisation at three levels: attribute, class and environment. Also, we present a data mart building interface which allows warehouse data to be reorganised through a multidimensional object-oriented model.


Alg\`ebre OLAP et langage graphique

Abstract: This article deals with OLAP systems based on multidimensional model. The conceptual model we provide, represents data through a constellation (multi-facts) composed of several multi-hierarchy dimensions. In this model, data are displayed through multidimensional tables. We define a query algebra handling these tables. This user oriented algebra is composed of a closure core of OLAP operators as soon as advanced operators dedicated to complex analysis. Finally, we specify a graphical OLAP language based on this algebra. This language facilitates analyses of decision makers.


Mod\'elisation et extraction de donn\'ees pour un entrep\^ot objet

Abstract: This paper describes an object-oriented model for designing complex and time-variant data warehouse data. The main contribution is the warehouse class concept, which extends the class concept by temporal and archive filters as well as a mapping function. Filters allow the keeping of relevant data changes whereas the mapping function defines the warehouse class schema from a global data source schema. The approach take into account static properties as well as dynamic properties. The behaviour extraction is based on the use-matrix concept.


Analyse multigraduelle OLAP

Abstract: Decisional systems are based on multidimensional databases improving OLAP analyses. The paper describes a new OLAP operator named "BLEND" to perform multigradual analyses. The operation transforms multidimensional structures during querying in order to analyse measures according to various granularity levels, which are reorganised into a single parameter. We study valid combinations of the operation in the context of strict hierarchies. First experimentations implement the operation in an R-OLAP framework showing the slight cost of this operation.


Contraintes pour mod\`ele et langage multidimensionnels

Abstract: This paper defines a constraint-based model dedicated to multidimensional databases. The model we define represents data through a constellation of facts (subjects of analyse) associated to dimensions (axis of analyse), which are possibly shared. Each dimension is organised according to several hierarchies (views of analyse) integrating several levels of data granularity. In order to insure data consistency, we introduce 5 semantic constraints (exclusion, inclusion, partition, simultaneity, totality) which can be intra-dimension or inter-dimensions; the intra-dimension constraints allow the expression of constraints between hierarchies within a same dimension whereas the inter-dimensions constraints focus on hierarchies of distinct dimensions. We also study repercussions of these constraints on multidimensional manipulations and we provide extensions of the multidimensional operators.


Mod\'elisation et manipulation de donn\'ees historis\'ees et archiv\'ees dans un entrep\^ot orient\'e objet

Abstract: This paper deals with temporal and archive object-oriented data warehouse modelling and querying. In a first step, we define a data model describing warehouses as central repositories of complex and temporal data extracted from one information source. The model is based on the concepts of warehouse object and environment. A warehouse object is composed of one current state, several past states (modelling value changes) and several archive states (summarising some value changes). An environment defines temporal parts in a warehouse schema according to a relevant granularity (attribute, class or graph). In a second step, we provide a query algebra dedicated to data warehouses. This algebra, which is based on common object algebras, integrates temporal operators and operators for querying object states. An other important contribution concerns dedicated operators allowing users to transform warehouse objects in temporal series as well as operators facilitating analytical treatments.


Elaboration d'entrep\^ots de donn\'ees complexes

Abstract: In this paper, we study the data warehouse modelling used in decision support systems. We provide an object-oriented data warehouse model allowing data warehouse description as a central repository of relevant, complex and temporal data. Our model integrates three concepts such as warehouse object, environment and warehouse class. Each warehouse object is composed of one current state, several past states (modelling its detailed evolutions) and several archive states (modelling its evolutions within a summarised form). The environment concept defines temporal parts in the data warehouse schema with significant granularities (attribute, class, graph). Finally, we provide five functions aiming at defining the data warehouse structures and two functions allowing the warehouse class inheritance hierarchy organisation.


Towards Conceptual Multidimensional Design in Decision Support Systems

Abstract: Multidimensional databases support efficiently on-line analytical processing (OLAP). In this paper, we depict a model dedicated to multidimensional databases. The approach we present designs decisional information through a constellation of facts and dimensions. Each dimension is possibly shared between several facts and it is organised according to multiple hierarchies. In addition, we define a comprehensive query algebra regrouping the more popular multidimensional operations in current commercial systems and research approaches. We introduce new operators dedicated to a constellation. Finally, we describe a prototype that allows managers to query constellations of facts, dimensions and multiple hierarchies.


On Finding Frequent Patterns in Directed Acyclic Graphs

Abstract: Given a directed acyclic graph with labeled vertices, we consider the problem of finding the most common label sequences ("traces") among all paths in the graph (of some maximum length m). Since the number of paths can be huge, we propose novel algorithms whose time complexity depends only on the size of the graph, and on the relative frequency epsilon of the most frequent traces. In addition, we apply techniques from streaming algorithms to achieve space usage that depends only on epsilon, and not on the number of distinct traces. The abstract problem considered models a variety of tasks concerning finding frequent patterns in event sequences. Our motivation comes from working with a data set of 2 million RFID readings from baggage trolleys at Copenhagen Airport. The question of finding frequent passenger movement patterns is mapped to the above problem. We report on experimental findings for this data set.


Size-Change Termination, Monotonicity Constraints and Ranking Functions

Abstract: Size-Change Termination (SCT) is a method of proving program termination based on the impossibility of infinite descent. To this end we may use a program abstraction in which transitions are described by monotonicity constraints over (abstract) variables. When only constraints of the form x>y' and x>=y' are allowed, we have size-change graphs. Both theory and practice are now more evolved in this restricted framework then in the general framework of monotonicity constraints. This paper shows that it is possible to extend and adapt some theory from the domain of size-change graphs to the general case, thus complementing previous work on monotonicity constraints. In particular, we present precise decision procedures for termination; and we provide a procedure to construct explicit global ranking functions from monotonicity constraints in singly-exponential time, which is better than what has been published so far even for size-change graphs.


Graph Sparsification by Edge-Connectivity and Random Spanning Trees

Abstract: We present new approaches to constructing graph sparsifiers --- weighted subgraphs for which every cut has the same value as the original graph, up to a factor of $(1 \pm \epsilon)$. Our first approach independently samples each edge $uv$ with probability inversely proportional to the edge-connectivity between $u$ and $v$. The fact that this approach produces a sparsifier resolves a question posed by Bencz\'ur and Karger (2002). Concurrent work of Hariharan and Panigrahi also resolves this question. Our second approach constructs a sparsifier by forming the union of several uniformly random spanning trees. Both of our approaches produce sparsifiers with $O(n \log^2(n)/\epsilon^2)$ edges. Our proofs are based on extensions of Karger's contraction algorithm, which may be of independent interest.


Node-Context Network Clustering using PARAFAC Tensor Decomposition

Abstract: We describe a clustering method for labeled link network (semantic graph) that can be used to group important nodes (highly connected nodes) with their relevant link's labels by using PARAFAC tensor decomposition. In this kind of network, the adjacency matrix can not be used to fully describe all information about the network structure. We have to expand the matrix into 3-way adjacency tensor, so that not only the information about to which nodes a node connects to but by which link's labels is also included. And by applying PARAFAC decomposition on this tensor, we get two lists, nodes and link's labels with scores attached to each node and labels, for each decomposition group. So clustering process to get the important nodes along with their relevant labels can be done simply by sorting the lists in decreasing order. To test the method, we construct labeled link network by using blog's dataset, where the blogs are the nodes and labeled links are the shared words among them. The similarity measures between the results and standard measures look promising, especially for two most important tasks, finding the most relevant words to blogs query and finding the most similar blogs to blogs query, about 0.87.


Software Requirements Specification of the IUfA's UUIS -- a Team 1 COMP5541-W10 Project Approach

Abstract: Unified University Inventory System (UUIS), is an inventory system created for the Imaginary University of Arctica (IUfA) to facilitate its inventory management, of all the faculties in one system. Team 1 elucidates the functions of the system and the characteristics of the users who have access to these functions. It shows the access restrictions to different functionalities of the system provided to users, who are the staff and students of the University. Team 1, also, emphasises on the necessary steps required to prevent the security of the system and its data.


Statistical Learning in Automated Troubleshooting: Application to LTE Interference Mitigation

Abstract: This paper presents a method for automated healing as part of off-line automated troubleshooting. The method combines statistical learning with constraint optimization. The automated healing aims at locally optimizing radio resource management (RRM) or system parameters of cells with poor performance in an iterative manner. The statistical learning processes the data using Logistic Regression (LR) to extract closed form (functional) relations between Key Performance Indicators (KPIs) and Radio Resource Management (RRM) parameters. These functional relations are then processed by an optimization engine which proposes new parameter values. The advantage of the proposed formulation is the small number of iterations required by the automated healing method to converge, making it suitable for off-line implementation. The proposed method is applied to heal an Inter-Cell Interference Coordination (ICIC) process in a 3G Long Term Evolution (LTE) network which is based on soft-frequency reuse scheme. Numerical simulations illustrate the benefits of the proposed approach.


Smart matching

Abstract: One of the most annoying aspects in the formalization of mathematics is the need of transforming notions to match a given, existing result. This kind of transformations, often based on a conspicuous background knowledge in the given scientific domain (mostly expressed in the form of equalities or isomorphisms), are usually implicit in the mathematical discourse, and it would be highly desirable to obtain a similar behavior in interactive provers. The paper describes the superposition-based implementation of this feature inside the Matita interactive theorem prover, focusing in particular on the so called smart application tactic, supporting smart matching between a goal and a given result.


The Deletable Bloom filter: A new member of the Bloom family

Abstract: We introduce the Deletable Bloom filter (DlBF) as a new spin on the popular data structure based on compactly encoding the information of where collisions happen when inserting elements. The DlBF design enables false-negative-free deletions at a fraction of the cost in memory consumption, which turns to be appealing for certain probabilistic filter applications.


Incremental Sampling-based Algorithms for Optimal Motion Planning

Abstract: During the last decade, incremental sampling-based motion planning algorithms, such as the Rapidly-exploring Random Trees (RRTs) have been shown to work well in practice and to possess theoretical guarantees such as probabilistic completeness. However, no theoretical bounds on the quality of the solution obtained by these algorithms have been established so far. The first contribution of this paper is a negative result: it is proven that, under mild technical conditions, the cost of the best path in the RRT converges almost surely to a non-optimal value. Second, a new algorithm is considered, called the Rapidly-exploring Random Graph (RRG), and it is shown that the cost of the best path in the RRG converges to the optimum almost surely. Third, a tree version of RRG is introduced, called the RRT$^*$ algorithm, which preserves the asymptotic optimality of RRG while maintaining a tree structure like RRT. The analysis of the new algorithms hinges on novel connections between sampling-based motion planning algorithms and the theory of random geometric graphs. In terms of computational complexity, it is shown that the number of simple operations required by both the RRG and RRT$^*$ algorithms is asymptotically within a constant factor of that required by RRT.


Lower Bounds on Near Neighbor Search via Metric Expansion

Abstract: In this paper we show how the complexity of performing nearest neighbor (NNS) search on a metric space is related to the expansion of the metric space. Given a metric space we look at the graph obtained by connecting every pair of points within a certain distance $r$ . We then look at various notions of expansion in this graph relating them to the cell probe complexity of NNS for randomized and deterministic, exact and approximate algorithms. For example if the graph has node expansion $\Phi$ then we show that any deterministic $t$-probe data structure for $n$ points must use space $S$ where $(St/n)^t > \Phi$. We show similar results for randomized algorithms as well. These relationships can be used to derive most of the known lower bounds in the well known metric spaces such as $l_1$, $l_2$, $l_\infty$ by simply computing their expansion. In the process, we strengthen and generalize our previous results (FOCS 2008). Additionally, we unify the approach in that work and the communication complexity based approach. Our work reduces the problem of proving cell probe lower bounds of near neighbor search to computing the appropriate expansion parameter. In our results, as in all previous results, the dependence on $t$ is weak; that is, the bound drops exponentially in $t$. We show a much stronger (tight) time-space tradeoff for the class of dynamic low contention data structures. These are data structures that supports updates in the data set and that do not look up any single cell too often.


Rankers over Infinite Words

Abstract: We consider the four fragments FO2, the intersection of Sigma2 and FO2, the intersection of Pi2 and FO2, and Delta2 of first-order logic FO[<] over finite and infinite words. For all four fragments, we give characterizations in terms of rankers. In particular, we generalize the notion of a ranker to infinite words in two possible ways. Both extensions are natural in the sense that over finite words, they coincide with classical rankers and over infinite words, they both have the full expressive power of FO2. Moreover, the first extension of rankers admits a characterization of the intersection of Sigma2 and FO2 while the other leads to a characterization of the intersection of Pi2 and FO2. Both versions of rankers yield characterizations of the fragment Delta2. As a byproduct, we also obtain characterizations based on unambiguous temporal logic and unambiguous interval temporal logic.


Local algorithms for the maximum flow and minimum cut in bounded-degree networks

Abstract: We show a deterministic constant-time local algorithm for constructing an approximately maximum flow and minimum fractional cut in multisource-multitarget networks with bounded degrees and bounded edge capacities. Locality means that the decision we make about each edge only depends on its constant radius neighborhood. We show two applications of the algorithms: one is related to the Aldous-Lyons Conjecture, and the other is about approximating the neighborhood distribution of graphs by bounded-size graphs. The scope of our results can be extended to unimodular random graphs and networks. As a corollary, we generalize the Maximum Flow Minimum Cut Theorem to unimodular random flow networks.


On Decidable Growth-Rate Properties of Imperative Programs

Abstract: In 2008, Ben-Amram, Jones and Kristiansen showed that for a simple "core" programming language - an imperative language with bounded loops, and arithmetics limited to addition and multiplication - it was possible to decide precisely whether a program had certain growth-rate properties, namely polynomial (or linear) bounds on computed values, or on the running time. This work emphasized the role of the core language in mitigating the notorious undecidability of program properties, so that one deals with decidable problems. A natural and intriguing problem was whether more elements can be added to the core language, improving its utility, while keeping the growth-rate properties decidable. In particular, the method presented could not handle a command that resets a variable to zero. This paper shows how to handle resets. The analysis is given in a logical style (proof rules), and its complexity is shown to be PSPACE-complete (in contrast, without resets, the problem was PTIME). The analysis algorithm evolved from the previous solution in an interesting way: focus was shifted from proving a bound to disproving it, and the algorithm works top-down rather than bottom-up.


Observation of implicit complexity by non confluence

Abstract: We propose to consider non confluence with respect to implicit complexity. We come back to some well known classes of first-order functional program, for which we have a characterization of their intentional properties, namely the class of cons-free programs, the class of programs with an interpretation, and the class of programs with a quasi-interpretation together with a termination proof by the product path ordering. They all correspond to PTIME. We prove that adding non confluence to the rules leads to respectively PTIME, NPTIME and PSPACE. Our thesis is that the separation of the classes is actually a witness of the intentional properties of the initial classes of programs.


General Ramified Recurrence is Sound for Polynomial Time

Abstract: Leivant's ramified recurrence is one of the earliest examples of an implicit characterization of the polytime functions as a subalgebra of the primitive recursive functions. Leivant's result, however, is originally stated and proved only for word algebras, i.e. free algebras whose constructors take at most one argument. This paper presents an extension of these results to ramified functions on any free algebras, provided the underlying terms are represented as graphs rather than trees, so that sharing of identical subterms can be exploited.


Safe Recursion on Notation into a Light Logic by Levels

Abstract: We embed Safe Recursion on Notation (SRN) into Light Affine Logic by Levels (LALL), derived from the logic L4. LALL is an intuitionistic deductive system, with a polynomial time cut elimination strategy. The embedding allows to represent every term t of SRN as a family of proof nets |t|^l in LALL. Every proof net |t|^l in the family simulates t on arguments whose bit length is bounded by the integer l. The embedding is based on two crucial features. One is the recursive type in LALL that encodes Scott binary numerals, i.e. Scott words, as proof nets. Scott words represent the arguments of t in place of the more standard Church binary numerals. Also, the embedding exploits the "fuzzy" borders of paragraph boxes that LALL inherits from L4 to "freely" duplicate the arguments, especially the safe ones, of t. Finally, the type of |t|^l depends on the number of composition and recursion schemes used to define t, namely the structural complexity of t. Moreover, the size of |t|^l is a polynomial in l, whose degree depends on the structural complexity of t. So, this work makes closer both the predicative recursive theoretic principles SRN relies on, and the proof theoretic one, called /stratification/, at the base of Light Linear Logic.


Church => Scott = Ptime: an application of resource sensitive realizability

Abstract: We introduce a variant of linear logic with second order quantifiers and type fixpoints, both restricted to purely linear formulas. The Church encodings of binary words are typed by a standard non-linear type `Church,' while the Scott encodings (purely linear representations of words) are by a linear type `Scott.' We give a characterization of polynomial time functions, which is derived from (Leivant and Marion 93): a function is computable in polynomial time if and only if it can be represented by a term of type Church => Scott. To prove soundness, we employ a resource sensitive realizability technique developed by Hofmann and Dal Lago.


Software Design Document, Testing, and Deployment and Configuration Management of the UUIS - a Team 1 COMP5541-W10 Project Approach

Abstract: The document presents a detailed description of the designs for the implementation of the Unified University Inventory System for the Imaginary University of Arctica. The document, through numerous diagrams and UI samples, gives the structure of the system and the functions of its modules. It also gives test cases and reports that support the system's architecture and design.


When can we decide that a P-finite sequence is positive?

Abstract: We consider two algorithms which can be used for proving positivity of sequences that are defined by a linear recurrence equation with polynomial coefficients (P-finite sequences). Both algorithms have in common that while they do succeed on a great many examples, there is no guarantee for them to terminate, and they do in fact not terminate for every input. For some restricted classes of P-finite recurrence equations of order up to three we provide a priori criteria that assert the termination of the algorithms.


Partial Denominator Bounds for Partial Linear Difference Equations

Abstract: We investigate which polynomials can possibly occur as factors in the denominators of rational solutions of a given partial linear difference equation (PLDE). Two kinds of polynomials are to be distinguished, we call them /periodic/ and /aperiodic/. The main result is a generalization of a well-known denominator bounding technique for univariate equations to PLDEs. This generalization is able to find all the aperiodic factors of the denominators for a given PLDE.


An approach to visualize the course of solving of a research task in humans

Abstract: A technique to study the dynamics of solving of a research task is suggested. The research task was based on specially developed software Right- Wrong Responder (RWR), with the participants having to reveal the response logic of the program. The participants interacted with the program in the form of a semi-binary dialogue, which implies the feedback responses of only two kinds - "right" or "wrong". The technique has been applied to a small pilot group of volunteer participants. Some of them have successfully solved the task (solvers) and some have not (non-solvers). In the beginning of the work, the solvers did more wrong moves than non-solvers, and they did less wrong moves closer to the finish of the work. A phase portrait of the work both in solvers and non-solvers showed definite cycles that may correspond to sequences of partially true hypotheses that may be formulated by the participants during the solving of the task.


Informal Concepts in Machines

Abstract: This paper constructively proves the existence of an effective procedure generating a computable (total) function that is not contained in any given effectively enumerable set of such functions. The proof implies the existence of machines that process informal concepts such as computable (total) functions beyond the limits of any given Turing machine or formal system, that is, these machines can, in a certain sense, "compute" function values beyond these limits. We call these machines creative. We argue that any "intelligent" machine should be capable of processing informal concepts such as computable (total) functions, that is, it should be creative. Finally, we introduce hypotheses on creative machines which were developed on the basis of theoretical investigations and experiments with computer programs. The hypotheses say that machine intelligence is the execution of a self-developing procedure starting from any universal programming language and any input.


Software Requirements Specification of the IUfA's UUIS -- a Team 3 COMP5541-W10 Project Approach

Abstract: The purpose of this document is to specify the requirements of the University Unified Inventory System, of the UIfA. The Team of Analysts used a Feedback Waterfall approach to collect the requirements. UML diagrams, such as Use case diagrams, Block Diagrams, Domain Models, and interface prototypes are some of the tools employed to develop the present document.


Improved Direct Product Theorems for Randomized Query Complexity

Abstract: The direct product problem is a fundamental question in complexity theory which seeks to understand how the difficulty of computing a function on each of k independent inputs scales with k. We prove the following direct product theorem (DPT) for query complexity: if every T-query algorithm has success probability at most 1 - eps in computing the Boolean function f on input distribution Mu, then for alpha <= 1, every (alpha eps Tk)-query algorithm has success probability at most (2^{alpha eps}(1 - eps))^k in computing the k-fold direct product f^k correctly on k independent inputs from Mu. In light of examples due to Shaltiel, this statement gives an essentially optimal tradeoff between the query bound and the error probability. As a corollary, we show that for an absolute constant alpha > 0, the worst-case success probability of any (alpha R_2(f)k)-query randomized algorithm for f^k falls exponentially with k. The best previous statement of this type, due to Klauck, Spalek, and de Wolf, required a query bound of O(bs(f)k). The proof involves defining and analyzing a collection of martingales associated with an algorithm attempting to solve f^k. Our method is quite general and yields a new XOR lemma and threshold DPT for the query model, as well as DPTs for the query complexity of learning tasks, search problems, and tasks involving interaction with dyamic entities. We also give a version of our DPT in which decision tree size is the resource of interest.


Proceedings International Workshop on Developments in Implicit Computational complExity

Abstract: This volume contains the proceedings of the International Workshop on Developments in Implicit Computational complExity (DICE 2010), which took place on March 27-28 2010 in Paphos, Cyprus, as a satellite event of the Joint European Conference on Theory and Practice of Software, ETAPS 2010. Implicit Computational Complexity aims at studying computational complexity without referring to external measuring conditions or particular machine models, but instead by considering restrictions on programming languages or logical principles implying complexity properties. The aim of this workshop was to bring together researchers working on implicit computational complexity, from its logical and semantical aspects to those related to the static analysis of programs, so as to foster their interaction and to give newcomers an overview of the current trends in this area.


Security Through Entertainment: Experiences Using a Memory Game for Secure Device Pairing

Abstract: The secure "pairing" of wireless devices based on auxiliary or out-of-band (OOB) communication, such as audio, visual, or tactile channels, is a well-established research direction. However, prior work shows that this approach to pairing can be prone to human errors of different forms that may directly or indirectly translate into man-in-the-middle attacks. To address this problem, we propose a general direction of the use of computer games for pairing. Since games are a popular means of entertainment, our hypothesis is that they may serve as an incentive to users and make the pairing process enjoyable for them, thus improving the usability, as well as the security, of the pairing process. We consider an emerging use case of pairing whereby two different users are involved, each in possession of his or her own device (e.g., Alice and Bob pairing their smartphones for social interactions). We develop "Alice Says," a pairing game based on a popular memory game called Simon (Says), and discuss the underlying design challenges. We also present a preliminary evaluation of Alice Says via a usability study and demonstrate its feasibility in terms of usability and security. Our results indicate that overall Alice Says was deemed as a fun and an enjoyable way to pair devices, confirming our hypothesis. However, contrary to our intuition, the relatively slower speed of Alice Says pairing was found to be a cause of concern and prompts the need for the design of faster pairing games. We put forth several ways in which this issue can be ameliorated. In addition, we also discuss several other security problems which are lacking optimal solutions and suggest ideas on how entertainment can be used to improve the current state of the art solutions that have been developed to address them.