Timed Parity Games: Complexity and Robustness

Abstract: We consider two-player games played in real time on game structures with clocks where the objectives of players are described using parity conditions. The games are \emph{concurrent} in that at each turn, both players independently propose a time delay and an action, and the action with the shorter delay is chosen. To prevent a player from winning by blocking time, we restrict each player to play strategies that ensure that the player cannot be responsible for causing a zeno run. First, we present an efficient reduction of these games to \emph{turn-based} (i.e., not concurrent) \emph{finite-state} (i.e., untimed) parity games. Our reduction improves the best known complexity for solving timed parity games. Moreover, the rich class of algorithms for classical parity games can now be applied to timed parity games. The states of the resulting game are based on clock regions of the original game, and the state space of the finite game is linear in the size of the region graph. Second, we consider two restricted classes of strategies for the player that represents the controller in a real-time synthesis problem, namely, \emph{limit-robust} and \emph{bounded-robust} winning strategies. Using a limit-robust winning strategy, the controller cannot choose an exact real-valued time delay but must allow for some nonzero jitter in each of its actions. If there is a given lower bound on the jitter, then the strategy is bounded-robust winning. We show that exact strategies are more powerful than limit-robust strategies, which are more powerful than bounded-robust winning strategies for any bound. For both kinds of robust strategies, we present efficient reductions to standard timed automaton games. These reductions provide algorithms for the synthesis of robust real-time controllers.


Flexible Session Management in a Distributed Environment

Abstract: Many secure communication libraries used by distributed systems, such as SSL, TLS, and Kerberos, fail to make a clear distinction between the authentication, session, and communication layers. In this paper we introduce CEDAR, the secure communication library used by the Condor High Throughput Computing software, and present the advantages to a distributed computing system resulting from CEDAR's separation of these layers. Regardless of the authentication method used, CEDAR establishes a secure session key, which has the flexibility to be used for multiple capabilities. We demonstrate how a layered approach to security sessions can avoid round-trips and latency inherent in network authentication. The creation of a distinct session management layer allows for optimizations to improve scalability by way of delegating sessions to other components in the system. This session delegation creates a chain of trust that reduces the overhead of establishing secure connections and enables centralized enforcement of system-wide security policies. Additionally, secure channels based upon UDP datagrams are often overlooked by existing libraries; we show how CEDAR's structure accommodates this as well. As an example of the utility of this work, we show how the use of delegated security sessions and other techniques inherent in CEDAR's architecture enables US CMS to meet their scalability requirements in deploying Condor over large-scale, wide-area grid systems.


E-Net Models of a Software System for Web Pages Security SECURITY

Abstract: This paper presents solutions for cryptography protection for web pages. The solutions comprise the authors' experience in development and implementation of systems for information security in the Automated Information Systems of Bulgarian Armed Forces. The architecture, the models and the methods are being explained.


An Empirical Study of Spam and Spam Vulnerable email Accounts

Abstract: Spam messages muddle up users inbox, consume network resources, and build up DDoS attacks, spread malware. Our goal is to present a definite figure about the characteristics of spam and spam vulnerable email accounts. These evaluations help us to enhance the existing technology to combat spam effectively. We collected 400 thousand spam mails from a spam trap set up in a corporate mail server for a period of 14 months form January 2006 to February 2007. Spammers use common techniques to spam end users regardless of corporate server and public mail server. So we believe that our spam collection is a sample of world wide spam traffic. Studying the characteristics of this sample helps us to better understand the features of spam and spam vulnerable e-mail accounts. We believe that this analysis is highly useful to develop more efficient anti spam techniques. In our analysis we classified spam based on attachment and contents. According to our study the four years old heavy users email accounts attract more spam than four years oldlight users mail accounts. The 14 months old relatively new email accounts don't receive spam. In some special cases like DDoS attacks, the new email accounts receive spam. During DDoS attack 14 months old heavy users email accounts have attracted more number of spam than 14 months old light users mail accounts.


Soil Classification Using GATree

Abstract: This paper details the application of a genetic programming framework for classification of decision tree of Soil data to classify soil texture. The database contains measurements of soil profile data. We have applied GATree for generating classification decision tree. GATree is a decision tree builder that is based on Genetic Algorithms (GAs). The idea behind it is rather simple but powerful. Instead of using statistic metrics that are biased towards specific trees we use a more flexible, global metric of tree quality that try to optimize accuracy and size. GATree offers some unique features not to be found in any other tree inducers while at the same time it can produce better results for many difficult problems. Experimental results are presented which illustrate the performance of generating best decision tree for classifying soil texture for soil data set.


A PDTB-Styled End-to-End Discourse Parser

Abstract: We have developed a full discourse parser in the Penn Discourse Treebank (PDTB) style. Our trained parser first identifies all discourse and non-discourse relations, locates and labels their arguments, and then classifies their relation types. When appropriate, the attribution spans to these relations are also determined. We present a comprehensive evaluation from both component-wise and error-cascading perspectives.


Probabilistic Inferences in Bayesian Networks

Abstract: Bayesian network is a complete model for the variables and their relationships, it can be used to answer probabilistic queries about them. A Bayesian network can thus be considered a mechanism for automatically applying Bayes' theorem to complex problems. In the application of Bayesian networks, most of the work is related to probabilistic inferences. Any variable updating in any node of Bayesian networks might result in the evidence propagation across the Bayesian networks. This paper sums up various inference techniques in Bayesian networks and provide guidance for the algorithm calculation in probabilistic inference in Bayesian networks.


Detecting Ontological Conflicts in Protocols between Semantic Web Services

Abstract: The task of verifying the compatibility between interacting web services has traditionally been limited to checking the compatibility of the interaction protocol in terms of message sequences and the type of data being exchanged. Since web services are developed largely in an uncoordinated way, different services often use independently developed ontologies for the same domain instead of adhering to a single ontology as standard. In this work we investigate the approaches that can be taken by the server to verify the possibility to reach a state with semantically inconsistent results during the execution of a protocol with a client, if the client ontology is published. Often database is used to store the actual data along with the ontologies instead of storing the actual data as a part of the ontology description. It is important to observe that at the current state of the database the semantic conflict state may not be reached even if the verification done by the server indicates the possibility of reaching a conflict state. A relational algebra based decision procedure is also developed to incorporate the current state of the client and the server databases in the overall verification procedure.


What's the point of documentation?

Abstract: We give a brief characterisation of the purposes and forms of documentation in and of spreadsheets.


On Three Alternative Characterizations of Combined Traces

Abstract: The combined trace (i.e., comtrace) notion was introduced by Janicki and Koutny in 1995 as a generalization of the Mazurkiewicz trace notion. Comtraces are congruence classes of step sequences, where the congruence relation is defined from two relations simultaneity and serializability on events. They also showed that comtraces correspond to some class of labeled stratified order structures, but left open the question of what class of labeled stratified orders represents comtraces. In this work, we proposed a class of labeled stratified order structures that captures exactly the comtrace notion. Our main technical contributions are representation theorems showing that comtrace quotient monoid, combined dependency graph (Kleijn and Koutny 2008) and our labeled stratified order structure characterization are three different and yet equivalent ways to represent comtraces. This paper is a revised and expanded version of L\^e (in Proceedings of PETRI NETS 2010, LNCS 6128, pp. 104-124).


Featureless 2D-3D Pose Estimation by Minimising an Illumination-Invariant Loss

Abstract: The problem of identifying the 3D pose of a known object from a given 2D image has important applications in Computer Vision ranging from robotic vision to image analysis. Our proposed method of registering a 3D model of a known object on a given 2D photo of the object has numerous advantages over existing methods: It does neither require prior training nor learning, nor knowledge of the camera parameters, nor explicit point correspondences or matching features between image and model. Unlike techniques that estimate a partial 3D pose (as in an overhead view of traffic or machine parts on a conveyor belt), our method estimates the complete 3D pose of the object, and works on a single static image from a given view, and under varying and unknown lighting conditions. For this purpose we derive a novel illumination-invariant distance measure between 2D photo and projected 3D model, which is then minimised to find the best pose parameters. Results for vehicle pose detection are presented.


Beyond Quantifier-Free Interpolation in Extensions of Presburger Arithmetic (Extended Technical Report)

Abstract: Craig interpolation has emerged as an effective means of generating candidate program invariants. We present interpolation procedures for the theories of Presburger arithmetic combined with (i) uninterpreted predicates (QPA+UP), (ii) uninterpreted functions (QPA+UF) and (iii) extensional arrays (QPA+AR). We prove that none of these combinations can be effectively interpolated without the use of quantifiers, even if the input formulae are quantifier-free. We go on to identify fragments of QPA+UP and QPA+UF with restricted forms of guarded quantification that are closed under interpolation. Formulae in these fragments can easily be mapped to quantifier-free expressions with integer division. For QPA+AR, we formulate a sound interpolation procedure that potentially produces interpolants with unrestricted quantifiers.


Characterizing Spam traffic and Spammers

Abstract: There is a tremendous increase in spam traffic these days. Spam messages muddle up users inbox, consume network resources, and build up DDoS attacks, spread worms and viruses. Our goal is to present a definite figure about the characteristics of spam and spammers. Since spammers change their mode of operation to counter anti spam technology,continues evaluation of the characteristics of spam and spammers technology has become mandatory. These evaluations help us to enhance the existing technology to combat spam effectively. We collected 400 thousand spam mails from a spam trap set up in a corporate mail server for a period of 14 months form January 2006 to February 2007. Spammers use common techniques to spam end users regardless of corporate server and public mail server. So we believe that our spam collection is a sample of world wide spam traffic. Studying the characteristics of this sample helps us to better understand the features of spam and spammers technology. We believe that this analysis could be useful to develop more efficient anti spam techniques.


An entropy based proof of the Moore bound for irregular graphs

Abstract: We provide proofs of the following theorems by considering the entropy of random walks: Theorem 1.(Alon, Hoory and Linial) Let G be an undirected simple graph with n vertices, girth g, minimum degree at least 2 and average degree d: Odd girth: If g=2r+1,then n \geq 1 + d*(\Sum_{i=0}^{r-1}(d-1)^i) Even girth: If g=2r,then n \geq 2*(\Sum_{i=0}^{r-1} (d-1)^i) Theorem 2.(Hoory) Let G = (V_L,V_R,E) be a bipartite graph of girth g = 2r, with n_L = |V_L| and n_R = |V_R|, minimum degree at least 2 and the left and right average degrees d_L and d_R. Then, n_L \geq \Sum_{i=0}^{r-1}(d_R-1)^{i/2}(d_L-1)^{i/2} n_R \geq \Sum_{i=0}^{r-1}(d_L-1)^{i/2}(d_R-1)^{i/2}


Price Differentiation for Communication Networks

Abstract: We study the optimal usage-based pricing problem in a resource-constrained network with one profit-maximizing service provider and multiple groups of surplus-maximizing users. With the assumption that the service provider knows the utility function of each user (thus complete information), we find that the complete price differentiation scheme can achieve a large revenue gain (e.g., 50%) compared to no price differentiation, when the total network resource is comparably limited and the high willingness to pay users are minorities. However, the complete price differentiation scheme may lead to a high implementational complexity. To trade off the revenue against the implementational complexity, we further study the partial price differentiation scheme, and design a polynomial-time algorithm that can compute the optimal partial differentiation prices. We also consider the incomplete information case where the service provider does not know which group each user belongs to. We show that it is still possible to realize price differentiation under this scenario, and provide the sufficient and necessary condition under which an incentive compatible differentiation scheme can achieve the same revenue as under complete information.


Group Anonymity

Abstract: In recent years the amount of digital data in the world has risen immensely. But, the more information exists, the greater is the possibility of its unwanted disclosure. Thus, the data privacy protection has become a pressing problem of the present time. The task of individual privacy-preserving is being thoroughly studied nowadays. At the same time, the problem of statistical disclosure control for collective (or group) data is still open. In this paper we propose an effective and relatively simple (wavelet-based) way to provide group anonymity in collective data. We also provide a real-life example to illustrate the method.


Providing Group Anonymity Using Wavelet Transform

Abstract: Providing public access to unprotected digital data can pose a threat of unwanted disclosing the restricted information. The problem of protecting such information can be divided into two main subclasses, namely, individual and group data anonymity. By group anonymity we define protecting important data patterns, distributions, and collective features which cannot be determined through analyzing individual records only. An effective and comparatively simple way of solving group anonymity problem is doubtlessly applying wavelet transform. It's easy-to-implement, powerful enough, and might produce acceptable results if used properly. In the paper, we present a novel method of using wavelet transform for providing group anonymity; it is gained through redistributing wavelet approximation values, along with simultaneous fixing data mean value and leaving wavelet details unchanged (or proportionally altering them). Moreover, we provide a comprehensive example to illustrate the method.


Group Anonymity: Problems and Solutions

Abstract: Existing methods of providing data anonymity preserve individual privacy, but, the task of protecting respondent groups' information in publicly available datasets remains open. Group anonymity lies in hiding (masking) data patterns that cannot be revealed by analyzing individual records. We discuss main corresponding problems, and provide methods for solving each one. Keywords: group anonymity, wavelet transform.


Providing Data Group Anonymity Using Concentration Differences

Abstract: Public access to digital data can turn out to be a cause of undesirable information disclosure. That's why it is vital to somehow protect the data before publishing. There exist two main subclasses of such a task, namely, providing individual and group anonymity. In the paper, we introduce a novel method of protecting group data patterns. Also, we provide a comprehensive illustrative example.


Data Group Anonymity: General Approach

Abstract: In the recent time, the problem of protecting privacy in statistical data before they are published has become a pressing one. Many reliable studies have been accomplished, and loads of solutions have been proposed. Though, all these researches take into consideration only the problem of protecting individual privacy, i.e., privacy of a single person, household, etc. In our previous articles, we addressed a completely new type of anonymity problems. We introduced a novel kind of anonymity to achieve in statistical data and called it group anonymity. In this paper, we aim at summarizing and generalizing our previous results, propose a complete mathematical description of how to provide group anonymity, and illustrate it with a couple of real-life examples.


Reciprocating Preferences Stablize Matching: College Admissions Revisited

Abstract: In considering the college admissions problem, almost fifty years ago, Gale and Shapley came up with a simple abstraction based on preferences of students and colleges. They introduced the concept of stability and optimality; and proposed the deferred acceptance (DA) algorithm that is proven to lead to a stable and optimal solution. This algorithm is simple and computationally efficient. Furthermore, in subsequent studies it is shown that the DA algorithm is also strategy-proof, which means, when the algorithm is played out as a mechanism for matching two sides (e.g. colleges and students), the parties (colleges or students) have no incentives to act other than according to their true preferences. Yet, in practical college admission systems, the DA algorithm is often not adopted. Instead, an algorithm known as the Boston Mechanism (BM) or its variants are widely adopted. In BM, colleges accept students without deferral (considering other colleges' decisions), which is exactly the opposite of Gale-Shapley's DA algorithm. To explain and rationalize this reality, we introduce the notion of reciprocating preference to capture the influence of a student's interest on a college's decision. This model is inspired by the actual mechanism used to match students to universities in Hong Kong. The notion of reciprocating preference defines a class of matching algorithms, allowing different degrees of reciprocating preferences by the students and colleges. DA and BM are but two extreme cases (with zero and a hundred percent reciprocation) of this set. This model extends the notion of stability and optimality as well. As in Gale-Shapley's original paper, we discuss how the analogy can be carried over to the stable marriage problem, thus demonstrating the model's general applicability.


Sorting by Transpositions is Difficult

Abstract: In comparative genomics, a transposition is an operation that exchanges two consecutive sequences of genes in a genome. The transposition distance, that is, the minimum number of transpositions needed to transform a genome into another, is, according to numerous studies, a relevant evolutionary distance. The problem of computing this distance when genomes are represented by permutations, called the Sorting by Transpositions problem, has been introduced by Bafna and Pevzner in 1995. It has naturally been the focus of a number of studies, but the computational complexity of this problem has remained undetermined for 15 years. In this paper, we answer this long-standing open question by proving that the Sorting by Transpositions problem is NP-hard. As a corollary of our result, we also prove that the following problem is NP-hard: given a permutation pi, is it possible to sort pi using db(pi)/3 permutations, where db(pi) is the number of breakpoints of pi?


Multiarmed Bandit Problems with Delayed Feedback

Abstract: In this paper we initiate the study of optimization of bandit type problems in scenarios where the feedback of a play is not immediately known. This arises naturally in allocation problems which have been studied extensively in the literature, albeit in the absence of delays in the feedback. We study this problem in the Bayesian setting. In presence of delays, no solution with provable guarantees is known to exist with sub-exponential running time. We show that bandit problems with delayed feedback that arise in allocation settings can be forced to have significant structure, with a slight loss in optimality. This structure gives us the ability to reason about the relationship of single arm policies to the entangled optimum policy, and eventually leads to a O(1) approximation for a significantly general class of priors. The structural insights we develop are of key interest and carry over to the setting where the feedback of an action is available instantaneously, and we improve all previous results in this setting as well.


Santa Claus Schedules Jobs on Unrelated Machines

Abstract: One of the classic results in scheduling theory is the 2-approximation algorithm by Lenstra, Shmoys, and Tardos for the problem of scheduling jobs to minimize makespan on unrelated machines, i.e., job j requires time p_{ij} if processed on machine i. More than two decades after its introduction it is still the algorithm of choice even in the restricted model where processing times are of the form p_{ij} in {p_j, \infty}. This problem, also known as the restricted assignment problem, is NP-hard to approximate within a factor less than 1.5 which is also the best known lower bound for the general version. Our main result is a polynomial time algorithm that estimates the optimal makespan of the restricted assignment problem within a factor 33/17 + \epsilon \approx 1.9412 + \epsilon, where \epsilon > 0 is an arbitrarily small constant. The result is obtained by upper bounding the integrality gap of a certain strong linear program, known as configuration LP, that was previously successfully used for the related Santa Claus problem. Similar to the strongest analysis for that problem our proof is based on a local search algorithm that will eventually find a schedule of the mentioned approximation guarantee, but is not known to converge in polynomial time.


Logics and Games for True Concurrency

Abstract: We study the underlying mathematical properties of various partial order models of concurrency based on transition systems, Petri nets, and event structures, and show that the concurrent behaviour of these systems can be captured in a uniform way by two simple and general dualities of local behaviour. Such dualities are used to define new mu-calculi and logic games for the analysis of concurrent systems with partial order semantics. Some results of this work are: the definition of a number of mu-calculi which, in some classes of systems, induce the same identifications as some of the best known bisimulation equivalences for concurrency; and the definition of (infinite) higher-order logic games for bisimulation and model-checking, where the players of the games are given (local) monadic second-order power on the sets of elements they are allowed to play. More specifically, we show that our games are sound and complete, and therefore, determined; moreover, they are decidable in the finite case and underpin novel decision procedures for bisimulation and model-checking. Since these mu-calculi and logic games generalise well-known fixpoint logics and game-theoretic decision procedures for concurrent systems with interleaving semantics, the results herein give some of the groundwork for the design of a logic-based, game-theoretic framework for studying, in a uniform way, several concurrent systems regardless of whether they have an interleaving or a partial order semantics.


Rank k Cholesky Up/Down-dating on the GPU: gpucholmodV0.2

Abstract: In this note we briefly describe our Cholesky modification algorithm for streaming multiprocessor architectures. Our implementation is available in C++ with Matlab binding, using CUDA to utilise the graphics processing unit (GPU). Limited speed ups are possible due to the bandwidth bound nature of the problem. Furthermore, a complex dependency pattern must be obeyed, requiring multiple kernels to be launched. Nonetheless, this makes for an interesting problem, and our approach can reduce the computation time by a factor of around 7 for matrices of size 5000 by 5000 and k=16, in comparison with the LINPACK suite running on a CPU of comparable vintage. Much larger problems can be handled however due to the O(n) scaling in required GPU memory of our method.


Hardness and Approximation of The Asynchronous Border Minimization Problem

Abstract: We study a combinatorial problem arising from microarrays synthesis. The synthesis is done by a light-directed chemical process. The objective is to minimize unintended illumination that may contaminate the quality of experiments. Unintended illumination is measured by a notion called border length and the problem is called Border Minimization Problem (BMP). The objective of the BMP is to place a set of probe sequences in the array and find an embedding (deposition of nucleotides/residues to the array cells) such that the sum of border length is minimized. A variant of the problem, called P-BMP, is that the placement is given and the concern is simply to find the embedding. Approximation algorithms have been previously proposed for the problem but it is unknown whether the problem is NP-hard or not. In this paper, we give a thorough study of different variations of BMP by giving NP-hardness proofs and improved approximation algorithms. We show that P-BMP, 1D-BMP, and BMP are all NP-hard. Contrast with the previous result that 1D-P-BMP is polynomial time solvable, the interesting implications include (i) the array dimension (1D or 2D) differentiates the complexity of P-BMP; (ii) for 1D array, whether placement is given differentiates the complexity of BMP; (iii) BMP is NP-hard regardless of the dimension of the array. Another contribution of the paper is improving the approximation for BMP from $O(n^{1/2} \log^2 n)$ to $O(n^{1/4} \log^2 n)$, where $n$ is the total number of sequences.


Streaming Algorithms from Precision Sampling

Abstract: A technique introduced by Indyk and Woodruff [STOC 2005] has inspired several recent advances in data-stream algorithms. We show that a number of these results follow easily from the application of a single probabilistic method called Precision Sampling. Using this method, we obtain simple data-stream algorithms that maintain a randomized sketch of an input vector $x=(x_1,...x_n)$, which is useful for the following applications. 1) Estimating the $F_k$-moment of $x$, for $k>2$. 2) Estimating the $\ell_p$-norm of $x$, for $p\in[1,2]$, with small update time. 3) Estimating cascaded norms $\ell_p(\ell_q)$ for all $p,q>0$. 4) $\ell_1$ sampling, where the goal is to produce an element $i$ with probability (approximately) $|x_i|/\|x\|_1$. It extends to similarly defined $\ell_p$-sampling, for $p\in [1,2]$. For all these applications the algorithm is essentially the same: scale the vector x entry-wise by a well-chosen random vector, and run a heavy-hitter estimation algorithm on the resulting vector. Our sketch is a linear function of x, thereby allowing general updates to the vector x. Precision Sampling itself addresses the problem of estimating a sum $\sum_{i=1}^n a_i$ from weak estimates of each real $a_i\in[0,1]$. More precisely, the estimator first chooses a desired precision $u_i\in(0,1]$ for each $i\in[n]$, and then it receives an estimate of every $a_i$ within additive $u_i$. Its goal is to provide a good approximation to $\sum a_i$ while keeping a tab on the "approximation cost" $\sum_i (1/u_i)$. Here we refine previous work [Andoni, Krauthgamer, and Onak, FOCS 2010] which shows that as long as $\sum a_i=\Omega(1)$, a good multiplicative approximation can be achieved using total precision of only $O(n\log n)$.


Optimal Deterministic Auctions with Correlated Priors

Abstract: We revisit the problem of designing the profit-maximizing single-item auction, solved by Myerson in his seminal paper for the case in which bidder valuations are independently distributed. We focus on general joint distributions, seeking the optimal deterministic incentive compatible auction. We give a geometric characterization of the optimal auction, resulting in a duality theorem and an efficient algorithm for finding the optimal deterministic auction in the two-bidder case and an NP-completeness result for three or more bidders.


Privately Releasing Conjunctions and the Statistical Query Barrier

Abstract: Suppose we would like to know all answers to a set of statistical queries C on a data set up to small error, but we can only access the data itself using statistical queries. A trivial solution is to exhaustively ask all queries in C. Can we do any better? + We show that the number of statistical queries necessary and sufficient for this task is---up to polynomial factors---equal to the agnostic learning complexity of C in Kearns' statistical query (SQ) model. This gives a complete answer to the question when running time is not a concern. + We then show that the problem can be solved efficiently (allowing arbitrary error on a small fraction of queries) whenever the answers to C can be described by a submodular function. This includes many natural concept classes, such as graph cuts and Boolean disjunctions and conjunctions. While interesting from a learning theoretic point of view, our main applications are in privacy-preserving data analysis: Here, our second result leads to the first algorithm that efficiently releases differentially private answers to of all Boolean conjunctions with 1% average error. This presents significant progress on a key open problem in privacy-preserving data analysis. Our first result on the other hand gives unconditional lower bounds on any differentially private algorithm that admits a (potentially non-privacy-preserving) implementation using only statistical queries. Not only our algorithms, but also most known private algorithms can be implemented using only statistical queries, and hence are constrained by these lower bounds. Our result therefore isolates the complexity of agnostic learning in the SQ-model as a new barrier in the design of differentially private algorithms.


Probabilistic Model Checking for Propositional Projection Temporal Logic

Abstract: Propositional Projection Temporal Logic (PPTL) is a useful formalism for reasoning about period of time in hardware and software systems and can handle both sequential and parallel compositions. In this paper, based on discrete time Markov chains, we investigate the probabilistic model checking approach for PPTL towards verifying arbitrary linear-time properties. We first define a normal form graph, denoted by NFG_inf, to capture the infinite paths of PPTL formulas. Then we present an algorithm to generate the NFG_inf. Since discrete-time Markov chains are the deterministic probabilistic models, we further give an algorithm to determinize and minimize the nondeterministic NFG_inf following the Safra's construction.


A short proof that adding some permutation rules to $\beta$ preserves $SN$

Abstract: I show that, if a term is $SN$ for $\beta$, it remains $SN$ when some permutation rules are added.


Optimal Binary Search Trees with Near Minimal Height

Abstract: Suppose we have n keys, n access probabilities for the keys, and n+1 access probabilities for the gaps between the keys. Let h_min(n) be the minimal height of a binary search tree for n keys. We consider the problem to construct an optimal binary search tree with near minimal height, i.e.\ with height h <= h_min(n) + Delta for some fixed Delta. It is shown, that for any fixed Delta optimal binary search trees with near minimal height can be constructed in time O(n^2). This is as fast as in the unrestricted case. So far, the best known algorithms for the construction of height-restricted optimal binary search trees have running time O(L n^2), whereby L is the maximal permitted height. Compared to these algorithms our algorithm is at least faster by a factor of log n, because L is lower bounded by log n.


Multivariate Analyis of Swap Bribery

Abstract: We consider the computational complexity of a problem modeling bribery in the context of voting systems. In the scenario of Swap Bribery, each voter assigns a certain price for swapping the positions of two consecutive candidates in his preference ranking. The question is whether it is possible, without exceeding a given budget, to bribe the voters in a way that the preferred candidate wins in the election. We initiate a parameterized and multivariate complexity analysis of Swap Bribery, focusing on the case of k-approval. We investigate how different cost functions affect the computational complexity of the problem. We identify a special case of k-approval for which the problem can be solved in polynomial time, whereas we prove NP-hardness for a slightly more general scenario. We obtain fixed-parameter tractability as well as W[1]-hardness results for certain natural parameters.


Transformation of Wiktionary entry structure into tables and relations in a relational database schema

Abstract: This paper addresses the question of automatic data extraction from the Wiktionary, which is a multilingual and multifunctional dictionary. Wiktionary is a collaborative project working on the same principles as the Wikipedia. The Wiktionary entry is a plain text from the text processing point of view. Wiktionary guidelines prescribe the entry layout and rules, which should be followed by editors of the dictionary. The presence of the structure of a Wiktionary article and formatting rules allows transforming the Wiktionary entry structure into tables and relations in a relational database schema, which is a part of a machine-readable dictionary (MRD). The paper describes how the flat text of the Wiktionary entry was extracted, converted, and stored in the specially designed relational database. The MRD contains the definitions, semantic relations, and translations extracted from the English and Russian Wiktionaries. The parser software is released under the open source license agreement (GPL), to facilitate its dissemination, modification and upgrades, to draw researchers and programmers into parsing other Wiktionaries, not only Russian and English.


Selling Privacy at Auction

Abstract: We initiate the study of markets for private data, though the lens of differential privacy. Although the purchase and sale of private data has already begun on a large scale, a theory of privacy as a commodity is missing. In this paper, we propose to build such a theory. Specifically, we consider a setting in which a data analyst wishes to buy information from a population from which he can estimate some statistic. The analyst wishes to obtain an accurate estimate cheaply. On the other hand, the owners of the private data experience some cost for their loss of privacy, and must be compensated for this loss. Agents are selfish, and wish to maximize their profit, so our goal is to design truthful mechanisms. Our main result is that such auctions can naturally be viewed and optimally solved as variants of multi-unit procurement auctions. Based on this result, we derive auctions for two natural settings which are optimal up to small constant factors: 1. In the setting in which the data analyst has a fixed accuracy goal, we show that an application of the classic Vickrey auction achieves the analyst's accuracy goal while minimizing his total payment. 2. In the setting in which the data analyst has a fixed budget, we give a mechanism which maximizes the accuracy of the resulting estimate while guaranteeing that the resulting sum payments do not exceed the analysts budget. In both cases, our comparison class is the set of envy-free mechanisms, which correspond to the natural class of fixed-price mechanisms in our setting. In both of these results, we ignore the privacy cost due to possible correlations between an individuals private data and his valuation for privacy itself. We then show that generically, no individually rational mechanism can compensate individuals for the privacy loss incurred due to their reported valuations for privacy.


Gradient Computation In Linear-Chain Conditional Random Fields Using The Entropy Message Passing Algorithm

Abstract: The paper proposes a numerically stable recursive algorithm for the exact computation of the linear-chain conditional random field gradient. It operates as a forward algorithm over the log-domain expectation semiring and has the purpose of enhancing memory efficiency when applied to long observation sequences. Unlike the traditional algorithm based on the forward-backward recursions, the memory complexity of our algorithm does not depend on the sequence length. The experiments on real data show that it can be useful for the problems which deal with long sequences.


Fuzzy Controller for Matrix Converter System to Improve its Quality of Output

Abstract: In this paper, Fuzzy Logic controller is developed for ac/ac Matrix Converter. Furthermore, Total Harmonic Distortion is reduced significantly. Space Vector Algorithm is a method to improve power quality of the converter output. But its quality is limited to 86.7%.We are introduced a Cross coupled DQ axis controller to improve power quality. The Matrix Converter is an attractive topology for High voltage transformation ratio. A Matlab / Simulink simulation analysis of the Matrix Converter system is provided. The design and implementation of fuzzy controlled Matrix Converter is described. This AC-AC system is proposed as an effective replacement for the conventional AC-DC-AC system which employs a two-step power conversion.


A Survey on Wireless Sensor Network Security

Abstract: Wireless sensor networks (WSNs) have recently attracted a lot of interest in the research community due their wide range of applications. Due to distributed nature of these networks and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their proper functioning. This problem is more critical if the network is deployed for some mission-critical applications such as in a tactical battlefield. Random failure of nodes is also very likely in real-life deployment scenarios. Due to resource constraints in the sensor nodes, traditional security mechanisms with large overhead of computation and communication are infeasible in WSNs. Security in sensor networks is, therefore, a particularly challenging task. This paper discusses the current state of the art in security mechanisms for WSNs. Various types of attacks are discussed and their countermeasures presented. A brief discussion on the future direction of research in WSN security is also included.


An Agent-Based Intrusion Detection System for Local Area Networks

Abstract: Since it is impossible to predict and identify all the vulnerabilities of a network beforehand, and penetration into a system by malicious intruders cannot always be prevented, intrusion detection systems (IDSs) are essential entities to ensure the security of a networked system. To be effective in carrying out their functions, the IDSs need to be accurate, adaptive, and extensible. Given these stringent requirements and the high level of vulnerabilities of the current days' networks, the design of an IDS has become a very challenging task. Although, an extensive research has been done on intrusion detection in a distributed environment, distributed IDSs suffer from a number of drawbacks e.g., high rates of false positives, low detection efficiency etc. In this paper, the design of a distributed IDS is proposed that consists of a group of autonomous and cooperating agents. In addition to its ability to detect attacks, the system is capable of identifying and isolating compromised nodes in the network thereby introducing fault-tolerance in its operations. The experiments conducted on the system have shown that it has a high detection efficiency and low false positives compared to some of the currently existing systems.


An Introduction to Software Engineering and Fault Tolerance

Abstract: This book consists of the chapters describing novel approaches to integrating fault tolerance into software development process. They cover a wide range of topics focusing on fault tolerance during the different phases of the software development, software engineering techniques for verification and validation of fault tolerance means, and languages for supporting fault tolerance specification and implementation. Accordingly, the book is structured into the following three parts: Part A: Fault tolerance engineering: from requirements to code; Part B: Verification and validation of fault tolerant systems; Part C: Languages and Tools for engineering fault tolerant systems.


Mixed Reality Serious Games: The Therapist Perspective

Abstract: The objective of this paper is to present a Mixed Reality System (MRS) for rehabilitation of the upper limb after stroke. The system answers the following challenges: (i) increase motivation of patients by making the training a personalized experience; (ii) take into account patients' impairments by offering intuitive and easy to use interaction modalities; (iii) make it possible to therapists to track patient's activities and to evaluate/track their progress; (iv) open opportunities for telemedicine and tele rehabilitation; (v) and provide an economically acceptable system by reducing both equipment and management costs. In order to test this system a pilot study has been conducted in conjunction with a French hospital in order to understand the potential and benefits of mixed reality. The pilot involved 3 therapists who 'played the role' of patients. Three sessions, one using conventional rehabilitation, another using an ad hoc developed game on a PC, and another using a mixed reality version of the same game were held. Results have shown the MRS and the PC game to be accepted more than physical rehabilitation.


Online Importance Weight Aware Updates

Abstract: An importance weight quantifies the relative importance of one example over another, coming up in applications of boosting, asymmetric classification costs, reductions, and active learning. The standard approach for dealing with importance weights in gradient descent is via multiplication of the gradient. We first demonstrate the problems of this approach when importance weights are large, and argue in favor of more sophisticated ways for dealing with them. We then develop an approach which enjoys an invariance property: that updating twice with importance weight $h$ is equivalent to updating once with importance weight $2h$. For many important losses this has a closed form update which satisfies standard regret guarantees when all examples have $h=1$. We also briefly discuss two other reasonable approaches for handling large importance weights. Empirically, these approaches yield substantially superior prediction with similar computational performance while reducing the sensitivity of the algorithm to the exact setting of the learning rate. We apply these to online active learning yielding an extraordinarily fast active learning algorithm that works even in the presence of adversarial noise.


The $z$-Transform and Automata-Recognizable Systems of Nonhomogeneous Linear Recurrence Equations over Semirings

Abstract: A nonhomogeneous system of linear recurrence equations can be recognized by an automaton $\mathcal{A}$ over a one-letter alphabet $A = \{z\}$. Conversely, the automaton $\mathcal{A}$ generates precisely this nonhomogeneous system of linear recurrence equations. We present the solutions of these systems and apply the $z$-transform to these solutions to obtain their series representation. Finally, we show some results that simplify the series representation of the $z$-transform of these solutions. We consider single systems as well as the composition of two systems.


Interference and Bandwidth Adjusted (ETX) in Wireless Multi-hop Networks

Abstract: In this paper, we propose a new quality link metric, interference and bandwidth adjusted ETX (IBETX) for wireless multi-hop networks. As MAC layer affects the link performance and consequently the route quality, the metric therefore, tackles the issue by achieving twofold MAC-awareness. Firstly, interference is calculated using cross-layered approach by sending probes to MAC layer. Secondly, the nominal bit rate information is provided to all nodes in the same contention domain by considering the bandwidth sharing mechanism of 802.11. Like ETX, our metric also calculates link delivery ratios that directly affect throughput and selects those routes that bypass dense regions in the network. Simulation results by NS-2 show that IBETX gives 19% higher throughput than ETX and 10% higher than Expected Throughput (ETP). Our metric also succeeds to reduce average end-to-end delay up to 16% less than Expected Link Performance (ELP) and 24% less than ETX.


Cause Clue Clauses: Error Localization using Maximum Satisfiability

Abstract: Much effort is spent everyday by programmers in trying to reduce long, failing execution traces to the cause of the error. We present a new algorithm for error cause localization based on a reduction to the maximal satisfiability problem (MAX-SAT), which asks what is the maximum number of clauses of a Boolean formula that can be simultaneously satisfied by an assignment. At an intuitive level, our algorithm takes as input a program and a failing test, and comprises the following three steps. First, using symbolic execution, we encode a trace of a program as a Boolean trace formula which is satisfiable iff the trace is feasible. Second, for a failing program execution (e.g., one that violates an assertion or a post-condition), we construct an unsatisfiable formula by taking the trace formula and additionally asserting that the input is the failing test and that the assertion condition does hold at the end. Third, using MAX-SAT, we find a maximal set of clauses in this formula that can be satisfied together, and output the complement set as a potential cause of the error. We have implemented our algorithm in a tool called bug-assist for C programs. We demonstrate the surprising effectiveness of the tool on a set of benchmark examples with injected faults, and show that in most cases, bug-assist can quickly and precisely isolate the exact few lines of code whose change eliminates the error. We also demonstrate how our algorithm can be modified to automatically suggest fixes for common classes of errors such as off-by-one.


A Stable Explicit Scheme for Solving Non-Homogeneous Constant Coefficients Equation using Green's Function

Abstract: A numerical explicit method to evaluates transient solutions of linear partial differential non-homogeneous equation with constant coefficients is proposed.


On the meaning of logical completeness

Abstract: Goedel's completeness theorem is concerned with provability, while Girard's theorem in ludics (as well as full completeness theorems in game semantics) are concerned with proofs. Our purpose is to look for a connection between these two disciplines. Following a previous work [3], we consider an extension of the original ludics with contraction and universal nondeterminism, which play dual roles, in order to capture a polarized fragment of linear logic and thus a constructive variant of classical propositional logic. We then prove a completeness theorem for proofs in this extended setting: for any behaviour (formula) A and any design (proof attempt) P, either P is a proof of A or there is a model M of the orthogonal of A which defeats P. Compared with proofs of full completeness in game semantics, ours exhibits a striking similarity with proofs of Goedel's completeness, in that it explicitly constructs a countermodel essentially using Koenig's lemma, proceeds by induction on formulas, and implies an analogue of Loewenheim-Skolem theorem.


Processor-Dependent Malware... and codes

Abstract: Malware usually target computers according to their operating system. Thus we have Windows malwares, Linux malwares and so on ... In this paper, we consider a different approach and show on a technical basis how easily malware can recognize and target systems selectively, according to the onboard processor chip. This technology is very easy to build since it does not rely on deep analysis of chip logical gates architecture. Floating Point Arithmetic (FPA) looks promising to define a set of tests to identify the processor or, more precisely, a subset of possible processors. We give results for different families of processors: AMD, Intel (Dual Core, Atom), Sparc, Digital Alpha, Cell, Atom ... As a conclusion, we propose two {\it open problems} that are new, to the authors' knowledge.


Reinforcement Learning Based on Active Learning Method

Abstract: In this paper, a new reinforcement learning approach is proposed which is based on a powerful concept named Active Learning Method (ALM) in modeling. ALM expresses any multi-input-single-output system as a fuzzy combination of some single-input-singleoutput systems. The proposed method is an actor-critic system similar to Generalized Approximate Reasoning based Intelligent Control (GARIC) structure to adapt the ALM by delayed reinforcement signals. Our system uses Temporal Difference (TD) learning to model the behavior of useful actions of a control system. The goodness of an action is modeled on Reward- Penalty-Plane. IDS planes will be updated according to this plane. It is shown that the system can learn with a predefined fuzzy system or without it (through random actions).


A New Sufficient Condition for 1-Coverage to Imply Connectivity

Abstract: An effective approach for energy conservation in wireless sensor networks is scheduling sleep intervals for extraneous nodes while the remaining nodes stay active to provide continuous service. For the sensor network to operate successfully the active nodes must maintain both sensing coverage and network connectivity, It proved before if the communication range of nodes is at least twice the sensing range, complete coverage of a convex area implies connectivity among the working set of nodes. In this paper we consider a rectangular region A = a *b, such that R a R b s s {\pounds}, {\pounds}, where s R is the sensing range of nodes. and put a constraint on minimum allowed distance between nodes(s). according to this constraint we present a new lower bound for communication range relative to sensing range of sensors(s 2 + 3 *R) that complete coverage of considered area implies connectivity among the working set of nodes; also we present a new distribution method, that satisfy our constraint.


CRAM: Compressed Random Access Memory

Abstract: We present a new data structure called the \emph{Compressed Random Access Memory} (CRAM) that can store a dynamic string $T$ of characters, e.g., representing the memory of a computer, in compressed form while achieving asymptotically almost-optimal bounds (in terms of empirical entropy) on the compression ratio. It allows short substrings of $T$ to be decompressed and retrieved efficiently and, significantly, characters at arbitrary positions of $T$ to be modified quickly during execution \emph{without decompressing the entire string}. This can be regarded as a new type of data compression that can update a compressed file directly. Moreover, at the cost of slightly increasing the time spent per operation, the CRAM can be extended to also support insertions and deletions. Our key observation that the empirical entropy of a string does not change much after a small change to the string, as well as our simple yet efficient method for maintaining an array of variable-length blocks under length modifications, may be useful for many other applications as well.


Use of Data Mining in Scheduler Optimization

Abstract: The operating system's role in a computer system is to manage the various resources. One of these resources is the Central Processing Unit. It is managed by a component of the operating system called the CPU scheduler. Schedulers are optimized for typical workloads expected to run on the platform. However, a single scheduler may not be appropriate for all workloads. That is, a scheduler may schedule a workload such that the completion time is minimized, but when another type of workload is run on the platform, scheduling and therefore completion time will not be optimal; a different scheduling algorithm, or a different set of parameters, may work better. Several approaches to solving this problem have been proposed. The objective of this survey is to summarize the approaches based on data mining, which are available in the literature. In addition to solutions that can be directly utilized for solving this problem, we are interested in data mining research in related areas that have potential for use in operating system scheduling. We also explain general technical issues involved in scheduling in modern computers, including parallel scheduling issues related to multi-core CPUs. We propose a taxonomy that classifies the scheduling approaches we discuss into different categories.


Regulating Response Time in an Autonomic Computing System: A Comparision of Proportional Control and Fuzzy Control Approaches

Abstract: Ecommerce is an area where an Autonomic Computing system could be very effectively deployed. Ecommerce has created demand for high quality information technology services and businesses are seeking quality of service guarantees from their service providers. These guarantees are expressed as part of service level agreements. Properly adjusting tuning parameters for enforcement of the service level agreement is time-consuming and skills-intensive. Moreover, in case of changes to the workload, the setting of the parameters may no longer be optimum. In an ecommerce system, where the workload changes frequently, there is a need to update the parameters at regular intervals. This paper describes two approaches, one, using a proportional controller and two, using a fuzzy controller, to automate the tuning of MaxClients parameter of Apache web server based on the required response time and the current workload. This is an illustration of the self-optimizing characteristic of an autonomic computing system.


Automatic Short -Answer Grading System (ASAGS)

Abstract: Automatic assessment needs short answer based evaluation and automated assessment. Various techniques used are Ontology, Semantic similarity matching and Statistical methods. An automatic short answer assessment system is attempted in this paper. Through experiments performed on a data set, we show that the semantic ASAGS outperforms methods based on simple lexical matching; resulting is up to 59 percent with respect to the traditional vector-based similarity metric.


OCamlJIT 2.0 - Faster Objective Caml

Abstract: This paper presents the current state of an ongoing research project to improve the performance of the OCaml byte-code interpreter using Just-In-Time native code generation. Our JIT engine OCamlJIT2 currently runs on x86-64 processors, mimicing precisely the behavior of the OCaml virtual machine. Its design and implementation is described, and performance measures are given.


An Algorithm for Detection of Selfish Nodes in Wireless Mesh Networks

Abstract: Wireless mesh networks (WMNs) are evolving as a key technology for next-generation wireless networks showing raid progress and numerous applications. These networks have the potential to provide robust and high-throughput data delivery to wireless users. In a WMN, high speed routers equipped with advanced antennas, communicate with each other in a multi-hop fashion over wireless channels and form a broadband backhaul. However, the throughput of a WMN may be severely degraded due to presence of some selfish routers that avoid forwarding packets for other nodes even as they send their own traffic through the network. This paper presents an algorithm for detection of selfish nodes in a WMN. It uses statistical theory of inference for reliable clustering of the nodes and is based on local observations by the nodes. Simulation results show that the algorithm has a high detection rate while having a low rate of false positives.


Finding topological subgraphs is fixed-parameter tractable

Abstract: We show that for every fixed undirected graph $H$, there is a $O(|V(G)|^3)$ time algorithm that tests, given a graph $G$, if $G$ contains $H$ as a topological subgraph (that is, a subdivision of $H$ is subgraph of $G$). This shows that topological subgraph testing is fixed-parameter tractable, resolving a longstanding open question of Downey and Fellows from 1992. As a corollary, for every $H$ we obtain an $O(|V(G)|^3)$ time algorithm that tests if there is an immersion of $H$ into a given graph $G$. This answers another open question raised by Downey and Fellows in 1992.


An Impossibility Result for Truthful Combinatorial Auctions with Submodular Valuations

Abstract: We show that every universally truthful randomized mechanism for combinatorial auctions with submodular valuations that provides $m^{\frac 1 2 -\epsilon}$ approximation to the social welfare and uses value queries only must use exponentially many value queries, where $m$ is the number of items. In contrast, ignoring incentives there exist constant ratio approximation algorithms for this problem. Our approach is based on a novel \emph{direct hardness} approach and completely skips the notoriously hard characterization step. The characterization step was the main obstacle for proving impossibility results in algorithmic mechanism design so far. We demonstrate two additional applications of our new technique: (1) an impossibility result for universally-truthful polynomial time flexible combinatorial public projects and (2) an impossibility result for truthful-in-expectation mechanisms for exact combinatorial public projects. The latter is the first result that bounds the power of polynomial-time truthful in expectation mechanisms in any setting.


Fundamentals of Mathematical Theory of Emotional Robots

Abstract: In this book we introduce a mathematically formalized concept of emotion, robot's education and other psychological parameters of intelligent robots. We also introduce unitless coefficients characterizing an emotional memory of a robot. Besides, the effect of a robot's memory upon its emotional behavior is studied, and theorems defining fellowship and conflicts in groups of robots are proved. Also unitless parameters describing emotional states of those groups are introduced, and a rule of making alternative (binary) decisions based on emotional selection is given. We introduce a concept of equivalent educational process for robots and a concept of efficiency coefficient of an educational process, and suggest an algorithm of emotional contacts within a group of robots. And generally, we present and describe a model of a virtual reality with emotional robots. The book is meant for mathematical modeling specialists and emotional robot software developers.


Asymptotically Optimal Randomized Rumor Spreading

Abstract: We propose a new protocol solving the fundamental problem of disseminating a piece of information to all members of a group of n players. It builds upon the classical randomized rumor spreading protocol and several extensions. The main achievements are the following: Our protocol spreads the rumor to all other nodes in the asymptotically optimal time of (1 + o(1)) \log_2 n. The whole process can be implemented in a way such that only O(n f(n)) calls are made, where f(n)= \omega(1) can be arbitrary. In contrast to other protocols suggested in the literature, our algorithm only uses push operations, i.e., only informed nodes take active actions in the network. To the best of our knowledge, this is the first randomized push algorithm that achieves an asymptotically optimal running time.


Reusing optical supports using a simple software

Abstract: In this paper we show how it is possible to reuse optical supports (CDs, DVDs, etc.) without using chemical or physical transformation, only employing a software that can easily run on domestic computers. This software can make obsolete optical supports useful again, converting de facto WEEE (Waste electric and electronic equipment) into EEE (Electric and electronic equipment). A massive use of such a software can lead to a significant change in EEE every-day use, reducing its production to sustainable levels.


Pushing BitTorrent Locality to the Limit

Abstract: Peer-to-peer (P2P) locality has recently raised a lot of interest in the community. Indeed, whereas P2P content distribution enables financial savings for the content providers, it dramatically increases the traffic on inter-ISP links. To solve this issue, the idea to keep a fraction of the P2P traffic local to each ISP was introduced a few years ago. Since then, P2P solutions exploiting locality have been introduced. However, several fundamental issues on locality still need to be explored. In particular, how far can we push locality, and what is, at the scale of the Internet, the reduction of traffic that can be achieved with locality? In this paper, we perform extensive experiments on a controlled environment with up to 10,000 BitTorrent clients to evaluate the impact of high locality on inter-ISP links traffic and peers download completion time. We introduce two simple mechanisms that make high locality possible in challenging scenarios and we show that we save up to several orders of magnitude inter-ISP traffic compared to traditional locality without adversely impacting peers download completion time. In addition, we crawled 214,443 torrents representing 6,113,224 unique peers spread among 9,605 ASes. We show that whereas the torrents we crawled generated 11.6 petabytes of inter-ISP traffic, our locality policy implemented for all torrents could have reduced the global inter-ISP traffic by up to 40%.


Normal art galleries: wall in - all in

Abstract: We introduce the notion of a normal gallery, a gallery in which any configuration of guards that visually covers the walls covers the entire gallery. We show that any star gallery is normal and any gallery with at most two reflex corners is normal. A polynomial time algorithm is provided deciding if, for a given polygon and a finite set of positions, there exists a configuration of guards in some of these positions that visually covers the walls but not the entire gallery.


Blackwell Approachability and Low-Regret Learning are Equivalent

Abstract: We consider the celebrated Blackwell Approachability Theorem for two-player games with vector payoffs. We show that Blackwell's result is equivalent, via efficient reductions, to the existence of "no-regret" algorithms for Online Linear Optimization. Indeed, we show that any algorithm for one such problem can be efficiently converted into an algorithm for the other. We provide a useful application of this reduction: the first efficient algorithm for calibrated forecasting.


An Optimization-Based Framework for Automated Market-Making

Abstract: Building on ideas from online convex optimization, we propose a general framework for the design of efficient securities markets over very large outcome spaces. The challenge here is computational. In a complete market, in which one security is offered for each outcome, the market institution can not efficiently keep track of the transaction history or calculate security prices when the outcome space is large. The natural solution is to restrict the space of securities to be much smaller than the outcome space in such a way that securities can be priced efficiently. Recent research has focused on searching for spaces of securities that can be priced efficiently by existing mechanisms designed for complete markets. While there have been some successes, much of this research has led to hardness results. In this paper, we take a drastically different approach. We start with an arbitrary space of securities with bounded payoff, and establish a framework to design markets tailored to this space. We prove that any market satisfying a set of intuitive conditions must price securities via a convex potential function and that the space of reachable prices must be precisely the convex hull of the security payoffs. We then show how the convex potential function can be defined in terms of an optimization over the convex hull of the security payoffs. The optimal solution to the optimization problem gives the security prices. Using this framework, we provide an efficient market for predicting the landing location of an object on a sphere. In addition, we show that we can relax our "no-arbitrage" condition to design a new efficient market maker for pair betting, which is known to be #P-hard to price using existing mechanisms. This relaxation also allows the market maker to charge transaction fees so that the depth of the market can be dynamically increased as the number of trades increases.


Mobility and Handoff Management in Wireless Networks

Abstract: With the increasing demands for new data and real-time services, wireless networks should support calls with different traffic characteristics and different Quality of Service (QoS)guarantees. In addition, various wireless technologies and networks exist currently that can satisfy different needs and requirements of mobile users. Since these different wireless networks act as complementary to each other in terms of their capabilities and suitability for different applications, integration of these networks will enable the mobile users to be always connected to the best available access network depending on their requirements. This integration of heterogeneous networks will, however, lead to heterogeneities in access technologies and network protocols. To meet the requirements of mobile users under this heterogeneous environment, a common infrastructure to interconnect multiple access networks will be needed. In this chapter, the design issues of a number of mobility management schemes have been presented. Each of these schemes utilizes IP-based technologies to enable efficient roaming in heterogeneous network. Efficient handoff mechanisms are essential for ensuring seamless connectivity and uninterrupted service delivery. A number of handoff schemes in a heterogeneous networking environment are also presented in this chapter.


Ubiquitous Computing: Potentials and Challenges

Abstract: The world is witnessing the birth of a revolutionary computing paradigm that promises to have a profound effect on the way we interact with computers, devices, physical spaces, and other people. This new technology, called ubiquitous computing, envisions a world where embedded processors, computers, sensors, and digital communications are inexpensive commodities that are available everywhere. This paper presents a comprehensive discussion on the central trends in ubiquitous computing considering them form technical, social and economic perspectives. It clearly identifies different application areas and sectors that will benefit from the potentials of ubiquitous computing. It also brings forth the challenges of ubiquitous computing that require active solutions and management.


Excerpt from the book World of Movable Objects

Abstract: This book is about the transformation of screen objects into movable and resizable and about the design of applications entirely on the basis of such elements. The screen objects have a wide variety of shapes; they can be either graphical objects or controls; there are solitary objects and very complex objects parts of which are involved in individual, synchronous, and related movements. Objects can be involved in forward movements and rotation; they can be resized and reconfigured; all these movements and situations are considered. On the basis of total movability, the new type of programs - user-driven applications - are designed. These applications continue to work according to their main purposes, but the whole control of WHAT, WHEN, and HOW must appear on the screen is passed from designers to users.


Evaluation of Lifetime Bounds of Wireless Sensor Networks

Abstract: In this paper we estimate lifetime bounds of a network of motes which communicate with each other using IEEE 802.15.4 standard. Different frame structures of IEEE 802.15.4 along with CSMA/CA medium access mechanism are investigated to discover the overhead of channel acquisition, header and footer of data frame, and transfer reliability during packet transmission. This overhead makes the fixed component, and the data payload makes the incremental component of a linear equation to estimate the power consumed during every packet transmission. Finally we input this per-packet power consumption in a mathematical model which estimates the lower and upper bounds of routings in the network. We also implemented a series of measurements on CC2420 radio used in a wide range of sensor motes to find the fixed and incremental components, and finally the lifetime of a network composed of the motes using this radio.


Towards Greener and Safer Mines

Abstract: Miniaturised sensors and networking are technical proven concepts. Both the technologies are proven and various components e.g., sensors, controls, etc. are commercially available. Technology scene in above areas presents enormous possibilities for developing innovative applications for real life situations. Mining operations in many countries have lot of scope for improving environmental and safety measures. Efforts have been made to develop a system to efficiently monitor a particular environment by deploying a wireless sensor network using commercially available components. Wireless Sensor Network has been integrated with telecom network through a gateway using a suitable topology which can be selected at the application layer. The developed system demonstrates a way to connect wireless sensor network to external network which enables the distant administrator to access real time data and act expediently from long-distance to improve the environmental situation or prevent a disaster. Potentially, it can be used to avoid the awful situations leading to terrible environment in underground mines. Keywords: Wireless sensor network, Mine safety, Environment monitoring and telecom.


Biopsym : a learning environment for transrectal ultrasound guided prostate biopsies

Abstract: This paper describes a learning environment for image-guided prostate biopsies in cancer diagnosis; it is based on an ultrasound probe simulator virtually exploring real datasets obtained from patients. The aim is to make the training of young physicians easier and faster with a tool that combines lectures, biopsy simulations and recommended exercises to master this medical gesture. It will particularly help acquiring the three-dimensional representation of the prostate needed for practicing biopsy sequences. The simulator uses a haptic feedback to compute the position of the virtual probe from three-dimensional (3D) ultrasound recorded data. This paper presents the current version of this learning environment.


Matching with Couples Revisited

Abstract: It is well known that a stable matching in a many-to-one matching market with couples need not exist. We introduce a new matching algorithm for such markets and show that for a general class of large random markets the algorithm will find a stable matching with high probability. In particular we allow the number of couples to grow at a near-linear rate. Furthermore, truth-telling is an approximated equilibrium in the game induced by the new matching algorithm. Our results are tight: for markets in which the number of couples grows at a linear rate, we show that with constant probability no stable matching exists.


A lower bound for the tree-width of planar graphs with vital linkages

Abstract: The disjoint paths problem asks, given an graph G and k + 1 pairs of terminals (s_0,t_0), ...,(s_k,t_k), whether there are k+1 pairwise disjoint paths P_0, ...,P_k, such that P_i connects s_i to t_i. Robertson and Seymour have proven that the problem can be solved in polynomial time if k is fixed. Nevertheless, the constants involved are huge, and the algorithm is far from implementable. The algorithm uses a bound on the tree-width of graphs with vital linkages, and deletion of irrelevant vertices. We give single exponential lower bounds both for the tree-width of planar graphs with vital linkages, and for the size of the grid necessary for finding irrelevant vertices.


Local Distributed Decision

Abstract: A central theme in distributed network algorithms concerns understanding and coping with the issue of locality. Inspired by sequential complexity theory, we focus on a complexity theory for distributed decision problems. In the context of locality, solving a decision problem requires the processors to independently inspect their local neighborhoods and then collectively decide whether a given global input instance belongs to some specified language. This paper introduces several classes of distributed decision problems, proves separation among them and presents some complete problems. More specifically, we consider the standard LOCAL model of computation and define LD (for local decision) as the class of decision problems that can be solved in constant number of communication rounds. We first study the intriguing question of whether randomization helps in local distributed computing, and to what extent. Specifically, we define the corresponding randomized class BPLD, and ask whether LD=BPLD. We provide a partial answer to this question by showing that in many cases, randomization does not help for deciding hereditary languages. In addition, we define the notion of local many-one reductions, and introduce the (nondeterministic) class NLD of decision problems for which there exists a certificate that can be verified in constant number of communication rounds. We prove that there exists an NLD-complete problem. We also show that there exist problems not in NLD. On the other hand, we prove that the class NLD#n, which is NLD assuming that each processor can access an oracle that provides the number of nodes in the network, contains all (decidable) languages. For this class we provide a natural complete problem as well.


Component Based Development

Abstract: Component Based Approach has been introduced in core engineering discipline long back but the introduction to component based concept in software perspective is recently developed by Object Management Group. Its benefits from the re-usability point of view is enormous. The intertwining relationship of domain engineering with component based software engineering is analyzed. The object oriented approach and its basic difference with component approach is of great concern. The present study highlights the life-cycle, cost effectiveness and the basic study of component based software from application perspective.


Online Scheduling on Identical Machines using SRPT

Abstract: Due to its optimality on a single machine for the problem of minimizing average flow time, Shortest-Remaining-Processing-Time (\srpt) appears to be the most natural algorithm to consider for the problem of minimizing average flow time on multiple identical machines. It is known that $\srpt$ achieves the best possible competitive ratio on multiple machines up to a constant factor. Using resource augmentation, $\srpt$ is known to achieve total flow time at most that of the optimal solution when given machines of speed $2- \frac{1}{m}$. Further, it is known that $\srpt$'s competitive ratio improves as the speed increases; $\srpt$ is $s$-speed $\frac{1}{s}$-competitive when $s \geq 2- \frac{1}{m}$. However, a gap has persisted in our understanding of $\srpt$. Before this work, the performance of $\srpt$ was not known when $\srpt$ is given $(1+\eps)$-speed when $0 < \eps < 1-\frac{1}{m}$, even though it has been thought that $\srpt$ is $(1+\eps)$-speed $O(1)$-competitive for over a decade. Resolving this question was suggested in Open Problem 2.9 from the survey "Online Scheduling" by Pruhs, Sgall, and Torng \cite{PruhsST}, and we answer the question in this paper. We show that $\srpt$ is \emph{scalable} on $m$ identical machines. That is, we show $\srpt$ is $(1+\eps)$-speed $O(\frac{1}{\eps})$-competitive for $\eps >0$. We complement this by showing that $\srpt$ is $(1+\eps)$-speed $O(\frac{1}{\eps^2})$-competitive for the objective of minimizing the $\ell_k$-norms of flow time on $m$ identical machines. Both of our results rely on new potential functions that capture the structure of \srpt. Our results, combined with previous work, show that $\srpt$ is the best possible online algorithm in essentially every aspect when migration is permissible.


Multiscale Gossip for Efficient Decentralized Averaging in Wireless Packet Networks

Abstract: This paper describes and analyzes a hierarchical gossip algorithm for solving the distributed average consensus problem in wireless sensor networks. The network is recursively partitioned into subnetworks. Initially, nodes at the finest scale gossip to compute local averages. Then, using geographic routing to enable gossip between nodes that are not directly connected, these local averages are progressively fused up the hierarchy until the global average is computed. We show that the proposed hierarchical scheme with $k$ levels of hierarchy is competitive with state-of-the-art randomized gossip algorithms, in terms of message complexity, achieving $\epsilon$-accuracy with high probability after $O\big(n \log \log n \log \frac{kn}{\epsilon} \big)$ messages. Key to our analysis is the way in which the network is recursively partitioned. We find that the optimal scaling law is achieved when subnetworks at scale $j$ contain $O(n^{(2/3)^j})$ nodes; then the message complexity at any individual scale is $O(n \log \frac{kn}{\epsilon})$, and the total number of scales in the hierarchy grows slowly, as $\Theta(\log \log n)$. Another important consequence of hierarchical construction is that the longest distance over which messages are exchanged is $O(n^{1/3})$ hops (at the highest scale), and most messages (at lower scales) travel shorter distances. In networks that use link-level acknowledgements, this results in less congestion and resource usage by reducing message retransmissions. Simulations illustrate that the proposed scheme is more message-efficient than existing state-of-the-art randomized gossip algorithms based on averaging along paths.


Introducing Business Language Driven Development

Abstract: A classical problem in Software Engineering is how to certify that every system requirement is correctly implemented by source code. This problem, albeit well studied, can still be considered an open one, given the problems faced by software development organizations. Trying to solve this problem, Behavior-Driven Development (BDD) is a specification technique that automatically certifies that all functional requirements are treated properly by source code, through the connection of the textual description of these requirements to automated tests. However, in some areas, such as Enterprise Information Systems, requirements are identified by Business Process Modeling - which uses graphical notations of the underlying business processes. Therefore, the aim of this paper is to present Business Language Driven Development (BLDD), a method that aims to extend BDD, by connecting business process models directly to source code, while keeping the expressiveness of text descriptions when they are better fitted than graphical artifacts.


A Distributed Method for Trust-Aware Recommendation in Social Networks

Abstract: This paper contains the details of a distributed trust-aware recommendation system. Trust-base recommenders have received a lot of attention recently. The main aim of trust-based recommendation is to deal the problems in traditional Collaborative Filtering recommenders. These problems include cold start users, vulnerability to attacks, etc.. Our proposed method is a distributed approach and can be easily deployed on social networks or real life networks such as sensor networks or peer to peer networks.


p-Norm Flow Optimization in a Network

Abstract: In this paper we study information flow paths in a data network, where traffic generated by servers (or sources) takes a multi-hop path in order to reach its clients (destinations). Each node in the middle of this multi-hop path should route the incoming traffic and the traffic generated by itself to the next hop in such a way that the traffic reaches its destination while avoiding congestion in the links. For simplicity, we will assume the network only carries single commodity traffic, i.e., all of the traffic should be routed to a single destination.


Pareto Optimal Solutions for Smoothed Analysts

Abstract: Consider an optimization problem with $n$ binary variables and $d+1$ linear objective functions. Each valid solution $x \in \{0,1\}^n$ gives rise to an objective vector in $\R^{d+1}$, and one often wants to enumerate the Pareto optima among them. In the worst case there may be exponentially many Pareto optima; however, it was recently shown that in (a generalization of) the smoothed analysis framework, the expected number is polynomial in $n$. Unfortunately, the bound obtained had a rather bad dependence on $d$; roughly $n^{d^d}$. In this paper we show a significantly improved bound of $n^{2d}$. Our proof is based on analyzing two algorithms. The first algorithm, on input a Pareto optimal $x$, outputs a "testimony" containing clues about $x$'s objective vector, $x$'s coordinates, and the region of space $B$ in which $x$'s objective vector lies. The second algorithm can be regarded as a {\em speculative} execution of the first -- it can uniquely reconstruct $x$ from the testimony's clues and just \emph{some} of the probability space's outcomes. The remainder of the probability space's outcomes are just enough to bound the probability that $x$'s objective vector falls into the region $B$.


Forward Kinematics Analysis and Tension Distribution of a Cable-Driven Sinking Winches Mechanism

Abstract: This paper concerns the forward kinematics and tension distribution of sinking winches mechanism, which is a type of four-cable-driven partly constrained parallel robot. Conventional studies on forward kinematics of cable-driven parallel robot assumed that all cables are taut. Actually, given the lengths of four cables, some cables may be slack when the platform is in static equilibrium. Therefore, in this paper, the tension state (tautness or slackness) of cables is considered in the forward kinematics model. We propose Traversal-Solving-Algorithm, which can indicate the tension state of cables, and further determine the pose of the platform, if the lengths of four cables are given. The effectiveness of the algorithm is verified by four examples. The results of this paper can be used to control sinking winches mechanism to achieve the level and stable motion of the platform, and to make the tension distribution of cables as uniform as possible.


Single Frame Image super Resolution using Learned Directionlets

Abstract: In this paper, a new directionally adaptive, learning based, single image super resolution method using multiple direction wavelet transform, called Directionlets is presented. This method uses directionlets to effectively capture directional features and to extract edge information along different directions of a set of available high resolution images .This information is used as the training set for super resolving a low resolution input image and the Directionlet coefficients at finer scales of its high-resolution image are learned locally from this training set and the inverse Directionlet transform recovers the super-resolved high resolution image. The simulation results showed that the proposed approach outperforms standard interpolation techniques like Cubic spline interpolation as well as standard Wavelet-based learning, both visually and in terms of the mean squared error (mse) values. This method gives good result with aliased images also.


Target tracking in the recommender space: Toward a new recommender system based on Kalman filtering

Abstract: In this paper, we propose a new approach for recommender systems based on target tracking by Kalman filtering. We assume that users and their seen resources are vectors in the multidimensional space of the categories of the resources. Knowing this space, we propose an algorithm based on a Kalman filter to track users and to predict the best prediction of their future position in the recommendation space.


What is a categorical model of the differential and the resource lambda-calculi?

Abstract: In this paper we provide an abstract model theory for the untyped differential lambda-calculus and the resource calculus. In particular we propose a general definition of model of these calculi, namely the notion of linear reflexive object in a Cartesian closed differential category. Examples of models based on relations are provided.


A framework for proving the self-organization of dynamic systems

Abstract: This paper aims at providing a rigorous definition of self- organization, one of the most desired properties for dynamic systems (e.g., peer-to-peer systems, sensor networks, cooperative robotics, or ad-hoc networks). We characterize different classes of self-organization through liveness and safety properties that both capture information re- garding the system entropy. We illustrate these classes through study cases. The first ones are two representative P2P overlays (CAN and Pas- try) and the others are specific implementations of \Omega (the leader oracle) and one-shot query abstractions for dynamic settings. Our study aims at understanding the limits and respective power of existing self-organized protocols and lays the basis of designing robust algorithm for dynamic systems.


Confluence Reduction for Probabilistic Systems (extended version)

Abstract: This paper presents a novel technique for state space reduction of probabilistic specifications, based on a newly developed notion of confluence for probabilistic automata. We prove that this reduction preserves branching probabilistic bisimulation and can be applied on-the-fly. To support the technique, we introduce a method for detecting confluent transitions in the context of a probabilistic process algebra with data, facilitated by an earlier defined linear format. A case study demonstrates that significant reductions can be obtained.


On the Dynamics of IP Address Allocation and Availability of End-Hosts

Abstract: The availability of end-hosts and their assigned routable IP addresses has impact on the ability to fight spammers and attackers, and on peer-to-peer application performance. Previous works study the availability of hosts mostly by using either active pinging or by studying access to a mail service, both approaches suffer from inherent inaccuracies. We take a different approach by measuring the IP addresses periodically reported by a uniquely identified group of the hosts running the DIMES agent. This fresh approach provides a chance to measure the true availability of end-hosts and the dynamics of their assigned routable IP addresses. Using a two month study of 1804 hosts, we find that over 60% of the hosts have a fixed IP address and 90% median availability, while some of the remaining hosts have more than 30 different IPs. For those that have periodically changing IP addresses, we find that the median average period per AS is roughly 24 hours, with a strong relation between the offline time and the probability of altering IP address.


Usability Meets Instant Gratification on the Semantic Web

Abstract: This paper presents a semantic wiki prototype application named SHAWN [later WikSAR] that allows structuring concepts within a wiki environment. To entice the use of Semantic Web technologies applications need to offer both high usability and instant gratification. Concept creation is exceptionally easy in SHAWN since metadata as well as plain text is entered within a single edit box on each wiki page in a self-explaining fashion. The entered data is immediately used for rendering sophisticated navigational means on the wiki. By editing simple wiki pages ontologies emerge.


Optimal Auctions with Correlated Bidders are Easy

Abstract: We consider the problem of designing a revenue-maximizing auction for a single item, when the values of the bidders are drawn from a correlated distribution. We observe that there exists an algorithm that finds the optimal randomized mechanism that runs in time polynomial in the size of the support. We leverage this result to show that in the oracle model introduced by Ronen and Saberi [FOCS'02], there exists a polynomial time truthful in expectation mechanism that provides a $(\frac 3 2+\epsilon)$-approximation to the revenue achievable by an optimal truthful-in-expectation mechanism, and a polynomial time deterministic truthful mechanism that guarantees $\frac 5 3$ approximation to the revenue achievable by an optimal deterministic truthful mechanism. We show that the $\frac 5 3$-approximation mechanism provides the same approximation ratio also with respect to the optimal truthful-in-expectation mechanism. This shows that the performance gap between truthful-in-expectation and deterministic mechanisms is relatively small. En route, we solve an open question of Mehta and Vazirani [EC'04]. Finally, we extend some of our results to the multi-item case, and show how to compute the optimal truthful-in-expectation mechanisms for bidders with more complex valuations.


Spin-the-bottle Sort and Annealing Sort: Oblivious Sorting via Round-robin Random Comparisons

Abstract: We study sorting algorithms based on randomized round-robin comparisons. Specifically, we study Spin-the-bottle sort, where comparisons are unrestricted, and Annealing sort, where comparisons are restricted to a distance bounded by a \emph{temperature} parameter. Both algorithms are simple, randomized, data-oblivious sorting algorithms, which are useful in privacy-preserving computations, but, as we show, Annealing sort is much more efficient. We show that there is an input permutation that causes Spin-the-bottle sort to require $\Omega(n^2\log n)$ expected time in order to succeed, and that in $O(n^2\log n)$ time this algorithm succeeds with high probability for any input. We also show there is an implementation of Annealing sort that runs in $O(n\log n)$ time and succeeds with very high probability.


Shape Calculus: Timed Operational Semantics and Well-formedness

Abstract: The Shape Calculus is a bio-inspired calculus for describing 3D shapes moving in a space. A shape forms a 3D process when combined with a behaviour. Behaviours are specified with a timed CCS-like process algebra using a notion of channel that models naturally binding sites on the surface of shapes. Processes can represent molecules or other mobile objects and can be part of networks of processes that move simultaneously and interact in a given geometrical space. The calculus embeds collision detection and response, binding of compatible 3D processes and splitting of previously established bonds. In this work the full formal timed operational semantics of the calculus is provided, together with examples that illustrate the use of the calculus in a well-known biological scenario. Moreover, a result of well-formedness about the evolution of a given network of well-formed 3D processes is proved.


Individual Privacy vs Population Privacy: Learning to Attack Anonymization

Abstract: Over the last decade there have been great strides made in developing techniques to compute functions privately. In particular, Differential Privacy gives strong promises about conclusions that can be drawn about an individual. In contrast, various syntactic methods for providing privacy (criteria such as kanonymity and l-diversity) have been criticized for still allowing private information of an individual to be inferred. In this report, we consider the ability of an attacker to use data meeting privacy definitions to build an accurate classifier. We demonstrate that even under Differential Privacy, such classifiers can be used to accurately infer "private" attributes in realistic data. We compare this to similar approaches for inferencebased attacks on other forms of anonymized data. We place these attacks on the same scale, and observe that the accuracy of inference of private attributes for Differentially Private data and l-diverse data can be quite similar.


Extended Active Learning Method

Abstract: Active Learning Method (ALM) is a soft computing method which is used for modeling and control, based on fuzzy logic. Although ALM has shown that it acts well in dynamic environments, its operators cannot support it very well in complex situations due to losing data. Thus ALM can find better membership functions if more appropriate operators be chosen for it. This paper substituted two new operators instead of ALM original ones; which consequently renewed finding membership functions in a way superior to conventional ALM. This new method is called Extended Active Learning Method (EALM).


Existence of Stable Exclusive Bilateral Exchanges in Networks

Abstract: In this paper we show that when individuals in a bipartite network exclusively choose partners and exchange valued goods with their partners, then there exists a set of exchanges that are pair-wise stable. Pair-wise stability implies that no individual breaks her partnership and no two neighbors in the network can form a new partnership while breaking other partnerships if any so that at least one of them improves her payoff and the other one does at least as good. We consider a general class of continuous, strictly convex and strongly monotone preferences over bundles of goods for individuals. Thus, this work extends the general equilibrium framework from markets to networks with exclusive exchanges. We present the complete existence proof using the existence of a generalized stable matching in \cite{Generalized-Stable-Matching}. The existence proof can be extended to problems in social games as in \cite{Matching-Equilibrium} and \cite{Social-Games}.


A Stable Explicit Scheme for Solving Inhomogeneous Constant Coefficients Differential Equation using Green's Function

Abstract: A numerical explicit method to evaluates transient solutions of linear partial differential inhomogeneous equation with constant coefficients is proposed. A general form of the scheme for a specific linear inhomogeneous equation is shown. The method is applied to the wave equation and the diffuse equation and is investigated by simulating simple models. The numerical solutions of the proposed method show good agreement to the exact solutions. Comparing with explicit FDM, FDM shows the instability by the violation of CFL condition whereas the proposed method is always stable irrespective of any time step width.


mVideoCast: Mobile, real time ROI detection and streaming

Abstract: A variety of applications are emerging to support streaming video from mobile devices. However, many tasks can benefit from streaming specific content rather than the full video feed which may include irrelevant, private, or distracting content. We describe a system that allows users to capture and stream targeted video content captured with a mobile device. The application incorporates a variety of automatic and interactive techniques to identify and segment desired content in the camera view, allowing the user to publish a more focused video.


On the Problem of Local Randomness in Privacy Amplification with an Active Adversary

Abstract: We study the problem of privacy amplification with an active adversary in the information theoretic setting. In this setting, two parties Alice and Bob start out with a shared $n$-bit weak random string $W$, and try to agree on a secret random key $R$ over a public channel fully controlled by an active and unbounded adversary. Typical assumptions are that these two parties have access to local private uniform random bits. In this paper we seek to minimize the requirements on the local randomness used by the two parties. We make two improvements over previous results. First, we reduce the number of random bits needed for each party to $\Theta(\ell+\log n)$, where $\ell$ is the security parameter, as long as $W$ has min-entropy $n^{\Omega(1)}$. Previously, the best known result needs to use $\Theta((\ell+\log n)\log n)$ bits. Our result is also asymptotically optimal. Second, we generalize the problem to the case where the two parties only have local weak random sources instead of truly uniform random bits. We show that when each party has a local weak random source with min-entropy $> n/2$, there is an efficient privacy amplification protocol that works nearly as good as if the two parties have access to local uniform random bits. Next, in the case where each party only has a weak random source with arbitrarily linear min-entropy, we give an efficient privacy amplification protocol where we can achieve security parameter up to $\Omega(\log k)$. Our results give the first protocols that achieve privacy amplification when each party only has access to a local weak random source.