Novel Analysis of Population Scalability in Evolutionary Algorithms

Abstract: Population-based evolutionary algorithms (EAs) have been widely applied to solve various optimization problems. The question of how the performance of a population-based EA depends on the population size arises naturally. The performance of an EA may be evaluated by different measures, such as the average convergence rate to the optimal set per generation or the expected number of generations to encounter an optimal solution for the first time. Population scalability is the performance ratio between a benchmark EA and another EA using identical genetic operators but a larger population size. Although intuitively the performance of an EA may improve if its population size increases, currently there exist only a few case studies for simple fitness functions. This paper aims at providing a general study for discrete optimisation. A novel approach is introduced to analyse population scalability using the fundamental matrix. The following two contributions summarize the major results of the current article. (1) We demonstrate rigorously that for elitist EAs with identical global mutation, using a lager population size always increases the average rate of convergence to the optimal set; and yet, sometimes, the expected number of generations needed to find an optimal solution (measured by either the maximal value or the average value) may increase, rather than decrease. (2) We establish sufficient and/or necessary conditions for the superlinear scalability, that is, when the average convergence rate of a $(\mu+\mu)$ EA (where $\mu\ge2$) is bigger than $\mu$ times that of a $(1+1)$ EA.


The fuzzy gene filter: A classifier performance assesment

Abstract: The Fuzzy Gene Filter (FGF) is an optimised Fuzzy Inference System designed to rank genes in order of differential expression, based on expression data generated in a microarray experiment. This paper examines the effectiveness of the FGF for feature selection using various classification architectures. The FGF is compared to three of the most common gene ranking algorithms: t-test, Wilcoxon test and ROC curve analysis. Four classification schemes are used to compare the performance of the FGF vis-a-vis the standard approaches: K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayesian Classifier (NBC) and Artificial Neural Network (ANN). A nested stratified Leave-One-Out Cross Validation scheme is used to identify the optimal number top ranking genes, as well as the optimal classifier parameters. Two microarray data sets are used for the comparison: a prostate cancer data set and a lymphoma data set.


Language Support for Declarative Future Commitments

Abstract: Sequential programming and work-flow programming are two useful, but radically different, ways of describing computational processing. Of the two, it is sequential programming that we teach all programmers and support by programming languages, whether in procedural, objectoriented, or functional paradigms. We teach workflow as a secondary style of problem decomposition for use in special situations, like distributed or networked processing. Both styles offer complementary advantages, but the fact that they employ radically different models for ownership of continuations interferes with our ability to integrate them in a way that allows them to be taught and used in a single programming language. This paper describes a programming language construct, declarative future commitments, that permit better integration of the two.


Ant Colony Optimization of Rough Set for HV Bushings Fault Detection

Abstract: Most transformer failures are attributed to bushings failures. Hence it is necessary to monitor the condition of bushings. In this paper three methods are developed to monitor the condition of oil filled bushing. Multi-layer perceptron (MLP), Radial basis function (RBF) and Rough Set (RS) models are developed and combined through majority voting to form a committee. The MLP performs better that the RBF and the RS is terms of classification accuracy. The RBF is the fasted to train. The committee performs better than the individual models. The diversity of models is measured to evaluate their similarity when used in the committee.


Improving the performance of the ripper in insurance risk classification : A comparitive study using feature selection

Abstract: The Ripper algorithm is designed to generate rule sets for large datasets with many features. However, it was shown that the algorithm struggles with classification performance in the presence of missing data. The algorithm struggles to classify instances when the quality of the data deteriorates as a result of increasing missing data. In this paper, a feature selection technique is used to help improve the classification performance of the Ripper model. Principal component analysis and evidence automatic relevance determination techniques are used to improve the performance. A comparison is done to see which technique helps the algorithm improve the most. Training datasets with completely observable data were used to construct the model and testing datasets with missing values were used for measuring accuracy. The results showed that principal component analysis is a better feature selection for the Ripper in improving the classification performance.


Optimal Algorithms for Ridge and Lasso Regression with Partially Observed Attributes

Abstract: We consider the most common variants of linear regression, including Ridge, Lasso and Support-vector regression, in a setting where the learner is allowed to observe only a fixed number of attributes of each example at training time. We present simple and efficient algorithms for these problems: for Lasso and Ridge regression they need the same total number of attributes (up to constants) as do full-information algorithms, for reaching a certain accuracy. For Support-vector regression, we require exponentially less attributes compared to the state of the art. By that, we resolve an open problem recently posed by Cesa-Bianchi et al. (2010). Experiments show the theoretical bounds to be justified by superior performance compared to the state of the art.


Automatically Creating Design Models from 3D Anthropometry Data

Abstract: When designing a product that needs to fit the human shape, designers often use a small set of 3D models, called design models, either in physical or digital form, as representative shapes to cover the shape variabilities of the population for which the products are designed. Until recently, the process of creating these models has been an art involving manual interaction and empirical guesswork. The availability of the 3D anthropometric databases provides an opportunity to create design models optimally. In this paper, we propose a novel way to use 3D anthropometric databases to generate design models that represent a given population for design applications such as the sizing of garments and gear. We generate the representative shapes by solving a covering problem in a parameter space. Well-known techniques in computational geometry are used to solve this problem. We demonstrate the method using examples in designing glasses and helmets.


XML content warehousing: Improving sociological studies of mailing lists and web data

Abstract: In this paper, we present the guidelines for an XML-based approach for the sociological study of Web data such as the analysis of mailing lists or databases available online. The use of an XML warehouse is a flexible solution for storing and processing this kind of data. We propose an implemented solution and show possible applications with our case study of profiles of experts involved in W3C standard-setting activity. We illustrate the sociological use of semi-structured databases by presenting our XML Schema for mailing-list warehousing. An XML Schema allows many adjunctions or crossings of data sources, without modifying existing data sets, while allowing possible structural evolution. We also show that the existence of hidden data implies increased complexity for traditional SQL users. XML content warehousing allows altogether exhaustive warehousing and recursive queries through contents, with far less dependence on the initial storage. We finally present the possibility of exporting the data stored in the warehouse to commonly-used advanced software devoted to sociological analysis.


Capacitated Domination: Constant Factor Approximation for Planar Graphs

Abstract: We consider the capacitated domination problem, which models a service-requirement assigning scenario and which is also a generalization of the dominating set problem. In this problem, we are given a graph with three parameters defined on the vertex set, which are cost, capacity, and demand. The objective of this problem is to compute a demand assignment of least cost, such that the demand of each vertex is fully-assigned to some of its closed neighbours without exceeding the amount of capacity they provide. In this paper, we provide the first constant factor approximation for this problem on planar graphs, based on a new perspective on the hierarchical structure of outer-planar graphs. We believe that this new perspective and technique can be applied to other capacitated covering problems to help tackle vertices of large degrees.


Artificial Neural Network and Rough Set for HV Bushings Condition Monitoring


Inapproximability of Orthogonal Compaction

Abstract: We show that several problems of compacting orthogonal graph drawings to use the minimum number of rows, area, length of longest edge or total edge length cannot be approximated better than within a polynomial factor of optimal in polynomial time unless P = NP. We also provide a fixed-parameter-tractable algorithm for testing whether a drawing can be compacted to a small number of rows.


Stepping Lazy Programs

Abstract: Debugging lazy functional programs poses serious challenges. In support of the "stop, examine, and resume" debugging style of imperative languages, some debugging tools abandon lazy evaluation. Other debuggers preserve laziness but present it in a way that may confuse programmers because the focus of evaluation jumps around in a seemingly random manner. In this paper, we introduce a supplemental tool, the algebraic program stepper. An algebraic stepper shows computation as a mathematical calculation. Algebraic stepping could be particularly useful for novice programmers or programmers new to lazy programming. Mathematically speaking, an algebraic stepper renders computation as the standard rewriting sequence of a lazy lambda-calculus. Our novel lazy semantics introduces lazy evaluation as a form of parallel program rewriting. It represents a compromise between Launchbury's store-based semantics and a simple, axiomatic description of lazy computation as sharing-via-parameters. Finally, we prove that the stepper's run-time machinery correctly reconstructs the standard rewriting sequence.


Mechanism Design via Consensus Estimates, Cross Checking, and Profit Extraction

Abstract: There is only one technique for prior-free optimal mechanism design that generalizes beyond the structurally benevolent setting of digital goods. This technique uses random sampling to estimate the distribution of agent values and then employs the Bayesian optimal mechanism for this estimated distribution on the remaining players. Though quite general, even for digital goods, this random sampling auction has a complicated analysis and is known to be suboptimal. To overcome these issues we generalize the consensus technique from Goldberg and Hartline (2003) to structurally rich environments that include, e.g., single-minded combinatorial auctions.


A Classification Framework for Web Browser Cross-Context Communication

Abstract: Demand for more advanced Web applications is the driving force behind Web browser evolution. Recent requirements for Rich Internet Applications, such as mashing-up data and background processing, are emphasizing the need for building and executing Web applications as a coordination of browser execution contexts. Since development of such Web applications depends on cross-context communication, many browser primitives and client-side frameworks have been developed to support this communication. In this paper we present a systematization of cross-context communication systems for Web browsers. Based on an analysis of previous research, requirements for modern Web applications and existing systems, we extract a framework for classifying cross-context communica-tion systems. Using the framework, we evaluate the current ecosystem of cross-context communication and outline directions for future Web research and engineering.


A fast algorithm for reversion of power series

Abstract: We give an algorithm for reversion of formal power series, based on an efficient way to implement the Lagrange inversion formula. Our algorithm requires $O(n^{1/2}(M(n) + MM(n^{1/2})))$ operations where $M(n)$ and $MM(n)$ are the costs of polynomial and matrix multiplication respectively. This matches the asymptotic complexity of an algorithm of Brent and Kung, but we achieve a constant factor speedup whose magnitude depends on the polynomial and matrix multiplication algorithms used. Benchmarks confirm that the algorithm performs well in practice.


Constraint Satisfaction Problems Parameterized Above or Below Tight Bounds: A Survey

Abstract: We consider constraint satisfaction problems parameterized above or below tight bounds. One example is MaxSat parameterized above $m/2$: given a CNF formula $F$ with $m$ clauses, decide whether there is a truth assignment that satisfies at least $m/2+k$ clauses, where $k$ is the parameter. Among other problems we deal with are MaxLin2-AA (given a system of linear equations over $\mathbb{F}_2$ in which each equation has a positive integral weight, decide whether there is an assignment to the variables that satisfies equations of total weight at least $W/2+k$, where $W$ is the total weight of all equations), Max-$r$-Lin2-AA (the same as MaxLin2-AA, but each equation has at most $r$ variables, where $r$ is a constant) and Max-$r$-Sat-AA (given a CNF formula $F$ with $m$ clauses in which each clause has at most $r$ literals, decide whether there is a truth assignment satisfying at least $\sum_{i=1}^m(1-2^{r_i})+k$ clauses, where $k$ is the parameter, $r_i$ is the number of literals in Clause $i$, and $r$ is a constant). We also consider Max-$r$-CSP-AA, a natural generalization of both Max-$r$-Lin2-AA and Max-$r$-Sat-AA, order (or, permutation) constraint satisfaction problems of arities 2 and 3 parameterized above the average value and some other problems related to MaxSat. We discuss results, both polynomial kernels and parameterized algorithms, obtained for the problems mainly in the last few years as well as some open questions.


dynPARTIX - A Dynamic Programming Reasoner for Abstract Argumentation

Abstract: The aim of this paper is to announce the release of a novel system for abstract argumentation which is based on decomposition and dynamic programming. We provide first experimental evaluations to show the feasibility of this approach.


Some Measurements of Nullable and Non-Nullable Parameter Declarations in Relation to Software Malleability

Abstract: The usual advantages put forward for including nullability declarations in the type systems of programming languages are that they improve program reliability or performance. But there is another, entirely different, reason for doing so. In the right context, this information enables the software artifacts we produce, the objects and methods, to exhibit much greater malleability. For declaratively typed languages, we can obtain greater software malleability by extending the model of method call so that assurance of a method's availability can be provided by any non-nullable parameter, not simply the target parameter, and by allowing the method's implementation to reside in classes or objects other than the target.. This paper examines the question of whether this hypothetical improvement in software malleability is consistent with existing programming practice by examining the question of the extent to which methods in existing software have multiplicities of non-nullable parameters. The circumstance occurs frequently enough to provide an important reason to introduce declarations of nullability into programming languages.


Publication patterns of award-winning forest scientists and implications for the ERA journal ranking

Abstract: Publication patterns of 79 forest scientists awarded major international forestry prizes during 1990-2010 were compared with the journal classification and ranking promoted as part of the 'Excellence in Research for Australia' (ERA) by the Australian Research Council. The data revealed that these scientists exhibited an elite publication performance during the decade before and two decades following their first major award. An analysis of their 1703 articles in 431 journals revealed substantial differences between the journal choices of these elite scientists and the ERA classification and ranking of journals. Implications from these findings are that additional cross-classifications should be added for many journals, and there should be an adjustment to the ranking of several journals relevant to the ERA Field of Research classified as 0705 Forestry Sciences.


Simplicity Effects in the Experience of Near-Miss

Abstract: Near-miss experiences are one of the main sources of intense emotions. Despite people's consistency when judging near-miss situations and when communicating about them, there is no integrated theoretical account of the phenomenon. In particular, individuals' reaction to near-miss situations is not correctly predicted by rationality-based or probability-based optimization. The present study suggests that emotional intensity in the case of near-miss is in part predicted by Simplicity Theory.


Emotion in good luck and bad luck: predictions from simplicity theory

Abstract: The feeling of good or bad luck occurs whenever there is an emotion contrast between an event and an easily accessible counterfactual alternative. This study suggests that cognitive simplicity plays a key role in the human ability to experience good and bad luck after the occurrence of an event.


A structural model of intuitive probability

Abstract: Though the ability of human beings to deal with probabilities has been put into question, the assessment of rarity is a crucial competence underlying much of human decision-making and is pervasive in spontaneous narrative behaviour. This paper proposes a new model of rarity and randomness assessment, designed to be cognitively plausible. Intuitive randomness is defined as a function of structural complexity. It is thus possible to assign probability to events without being obliged to consider the set of alternatives. The model is tested on Lottery sequences and compared with subjects' preferences.


Computing with Logic as Operator Elimination: The ToyElim System

Abstract: A prototype system is described whose core functionality is, based on propositional logic, the elimination of second-order operators, such as Boolean quantifiers and operators for projection, forgetting and circumscription. This approach allows to express many representational and computational tasks in knowledge representation - for example computation of abductive explanations and models with respect to logic programming semantics - in a uniform operational system, backed by a uniform classical semantic framework.


Making Use of Advances in Answer-Set Programming for Abstract Argumentation Systems

Abstract: Dung's famous abstract argumentation frameworks represent the core formalism for many problems and applications in the field of argumentation which significantly evolved within the last decade. Recent work in the field has thus focused on implementations for these frameworks, whereby one of the main approaches is to use Answer-Set Programming (ASP). While some of the argumentation semantics can be nicely expressed within the ASP language, others required rather cumbersome encoding techniques. Recent advances in ASP systems, in particular, the metasp optimization frontend for the ASP-package gringo/claspD provides direct commands to filter answer sets satisfying certain subset-minimality (or -maximality) constraints. This allows for much simpler encodings compared to the ones in standard ASP language. In this paper, we experimentally compare the original encodings (for the argumentation semantics based on preferred, semi-stable, and respectively, stage extensions) with new metasp encodings. Moreover, we provide novel encodings for the recently introduced resolution-based grounded semantics. Our experimental results indicate that the metasp approach works well in those cases where the complexity of the encoded problem is adequately mirrored within the metasp approach.


Non-trivial two-armed partial-monitoring games are bandits

Abstract: We consider online learning in partial-monitoring games against an oblivious adversary. We show that when the number of actions available to the learner is two and the game is nontrivial then it is reducible to a bandit-like game and thus the minimax regret is $\Theta(\sqrt{T})$.


Algorithms for the Problems of Length-Constrained Heaviest Segments

Abstract: We present algorithms for length-constrained maximum sum segment and maximum density segment problems, in particular, and the problem of finding length-constrained heaviest segments, in general, for a sequence of real numbers. Given a sequence of n real numbers and two real parameters L and U (L <= U), the maximum sum segment problem is to find a consecutive subsequence, called a segment, of length at least L and at most U such that the sum of the numbers in the subsequence is maximum. The maximum density segment problem is to find a segment of length at least L and at most U such that the density of the numbers in the subsequence is the maximum. For the first problem with non-uniform width there is an algorithm with time and space complexities in O(n). We present an algorithm with time complexity in O(n) and space complexity in O(U). For the second problem with non-uniform width there is a combinatorial solution with time complexity in O(n) and space complexity in O(U). We present a simple geometric algorithm with the same time and space complexities. We extend our algorithms to respectively solve the length-constrained k maximum sum segments problem in O(n+k) time and O(max{U, k}) space, and the length-constrained $k$ maximum density segments problem in O(n min{k, U-L}) time and O(U+k) space. We present extensions of our algorithms to find all the length-constrained segments having user specified sum and density in O(n+m) and O(nlog (U-L)+m) times respectively, where m is the number of output. Previously, there was no known algorithm with non-trivial result for these problems. We indicate the extensions of our algorithms to higher dimensions. All the algorithms can be extended in a straight forward way to solve the problems with non-uniform width and non-uniform weight.


A $(k + 3)/2$-approximation algorithm for monotone submodular maximization over a $k$-exchange system

Abstract: We consider the problem of maximizing a monotone submodular function in a $k$-exchange system. These systems, introduced by Feldman et al., generalize the matroid k-parity problem in a wide class of matroids and capture many other combinatorial optimization problems. Feldman et al. show that a simple non-oblivious local search algorithm attains a $(k + 1)/2$ approximation ratio for the problem of linear maximization in a $k$-exchange system. Here, we extend this approach to the case of monotone submodular objective functions. We give a deterministic, non-oblivious local search algorithm that attains an approximation ratio of $(k + 3)/2$ for the problem of maximizing a monotone submodular function in a $k$-exchange system.


Verbal Characterization of Probabilistic Clusters using Minimal Discriminative Propositions

Abstract: In a knowledge discovery process, interpretation and evaluation of the mined results are indispensable in practice. In the case of data clustering, however, it is often difficult to see in what aspect each cluster has been formed. This paper proposes a method for automatic and objective characterization or "verbalization" of the clusters obtained by mixture models, in which we collect conjunctions of propositions (attribute-value pairs) that help us interpret or evaluate the clusters. The proposed method provides us with a new, in-depth and consistent tool for cluster interpretation/evaluation, and works for various types of datasets including continuous attributes and missing values. Experimental results with a couple of standard datasets exhibit the utility of the proposed method, and the importance of the feedbacks from the interpretation/evaluation step.


Une analyse bas\'ee sur la S-DRT pour la mod\'elisation de dialogues pathologiques

Abstract: In this article, we present a corpus of dialogues between a schizophrenic speaker and an interlocutor who drives the dialogue. We had identified specific discontinuities for paranoid schizophrenics. We propose a modeling of these discontinuities with S-DRT (its pragmatic part)


Event in Compositional Dynamic Semantics

Abstract: We present a framework which constructs an event-style dis- course semantics. The discourse dynamics are encoded in continuation semantics and various rhetorical relations are embedded in the resulting interpretation of the framework. We assume discourse and sentence are distinct semantic objects, that play different roles in meaning evalua- tion. Moreover, two sets of composition functions, for handling different discourse relations, are introduced. The paper first gives the necessary background and motivation for event and dynamic semantics, then the framework with detailed examples will be introduced.


Encoding Phases using Commutativity and Non-commutativity in a Logical Framework

Abstract: This article presents an extension of Minimalist Categorial Gram- mars (MCG) to encode Chomsky's phases. These grammars are based on Par- tially Commutative Logic (PCL) and encode properties of Minimalist Grammars (MG) of Stabler. The first implementation of MCG were using both non- commutative properties (to respect the linear word order in an utterance) and commutative ones (to model features of different constituents). Here, we pro- pose to adding Chomsky's phases with the non-commutative tensor product of the logic. Then we could give account of the PIC just by using logical prop- erties of the framework.


RBO Protocol: Broadcasting Huge Databases for Tiny Receivers

Abstract: We propose a protocol (called RBO) for broadcasting long streams of single-packet messages over radio channel for tiny, battery powered, receivers. The messages are labeled by the keys from some linearly ordered set. The sender repeatedly broadcasts a sequence of many (possibly millions) of messages, while each receiver is interested in reception of a message with a specified key within this sequence. The transmission is arranged so that the receiver can wake up in arbitrary moment and find the nearest transmission of its searched message. Even if it does not know the position of the message in the sequence, it needs only to receive a small number of (the headers of) other messages to locate it properly. Thus it can save energy by keeping the radio switched off most of the time. We show that bit-reversal permutation has "recursive bisection properties" and, as a consequence, RBO can be implemented very efficiently with only constant number of $\log_2 n$-bit variables, where $n$ is the total number of messages in the sequence. The total number of the required receptions is at most $2\log_2 n +2$ in the model with perfect synchronization. The basic procedure of RBO (computation of the time slot for the next required reception) requires only $O(\log^3 n)$ bit-wise operations. We propose implementation mechanisms for realistic model (with imperfect synchronization), for operating systems (such as e.g. TinyOS).


Minimalist Grammars and Minimalist Categorial Grammars, definitions toward inclusion of generated languages

Abstract: Stabler proposes an implementation of the Chomskyan Minimalist Program, Chomsky 95 with Minimalist Grammars - MG, Stabler 97. This framework inherits a long linguistic tradition. But the semantic calculus is more easily added if one uses the Curry-Howard isomorphism. Minimalist Categorial Grammars - MCG, based on an extension of the Lambek calculus, the mixed logic, were introduced to provide a theoretically-motivated syntax-semantics interface, Amblard 07. In this article, we give full definitions of MG with algebraic tree descriptions and of MCG, and take the first steps towards giving a proof of inclusion of their generated languages.


CD-PHY: Physical Layer Security in Wireless Networks through Constellation Diversity

Abstract: A common approach for introducing security at the physical layer is to rely on the channel variations of the wireless environment. This type of approach is not always suitable for wireless networks where the channel remains static for most of the network lifetime. For these scenarios, a channel independent physical layer security measure is more appropriate which will rely on a secret known to the sender and the receiver but not to the eavesdropper. In this paper, we propose CD-PHY, a physical layer security technique that exploits the constellation diversity of wireless networks which is independent of the channel variations. The sender and the receiver use a custom bit sequence to constellation symbol mapping to secure the physical layer communication which is not known a priori to the eavesdropper. Through theoretical modeling and experimental simulation, we show that this information theoretic construct can achieve Shannon secrecy and any brute force attack from the eavesdropper incurs high overhead and minuscule probability of success. Our results also show that the high bit error rate also makes decoding practically infeasible for the eavesdropper, thus securing the communication between the sender and receiver.


Optimal Coalition Structures in Cooperative Graph Games

Abstract: Representation languages for coalitional games are a key research area in algorithmic game theory. There is an inherent tradeoff between how general a language is, allowing it to capture more elaborate games, and how hard it is computationally to optimize and solve such games. One prominent such language is the simple yet expressive Weighted Graph Games (WGGs) representation [14], which maintains knowledge about synergies between agents in the form of an edge weighted graph. We consider the problem of finding the optimal coalition structure in WGGs. The agents in such games are vertices in a graph, and the value of a coalition is the sum of the weights of the edges present between coalition members. The optimal coalition structure is a partition of the agents to coalitions, that maximizes the sum of utilities obtained by the coalitions. We show that finding the optimal coalition structure is not only hard for general graphs, but is also intractable for restricted families such as planar graphs which are amenable for many other combinatorial problems. We then provide algorithms with constant factor approximations for planar, minor-free and bounded degree graphs.


Single-trial EEG Discrimination between Wrist and Finger Movement Imagery and Execution in a Sensorimotor BCI

Abstract: A brain-computer interface (BCI) may be used to control a prosthetic or orthotic hand using neural activity from the brain. The core of this sensorimotor BCI lies in the interpretation of the neural information extracted from electroencephalogram (EEG). It is desired to improve on the interpretation of EEG to allow people with neuromuscular disorders to perform daily activities. This paper investigates the possibility of discriminating between the EEG associated with wrist and finger movements. The EEG was recorded from test subjects as they executed and imagined five essential hand movements using both hands. Independent component analysis (ICA) and time-frequency techniques were used to extract spectral features based on event-related (de)synchronisation (ERD/ERS), while the Bhattacharyya distance (BD) was used for feature reduction. Mahalanobis distance (MD) clustering and artificial neural networks (ANN) were used as classifiers and obtained average accuracies of 65 % and 71 % respectively. This shows that EEG discrimination between wrist and finger movements is possible. The research introduces a new combination of motor tasks to BCI research.


A Frequent Closed Itemsets Lattice-based Approach for Mining Minimal Non-Redundant Association Rules

Abstract: There are many algorithms developed for improvement the time of mining frequent itemsets (FI) or frequent closed itemsets (FCI). However, the algorithms which deal with the time of generating association rules were not put in deep research. In reality, in case of a database containing many FI/FCI (from ten thousands up to millions), the time of generating association rules is much larger than that of mining FI/FCI. Therefore, this paper presents an application of frequent closed itemsets lattice (FCIL) for mining minimal non-redundant association rules (MNAR) to reduce a lot of time for generating rules. Firstly, we use CHARM-L for building FCIL. After that, based on FCIL, an algorithm for fast generating MNAR will be proposed. Experimental results show that the proposed algorithm is much faster than frequent itemsets lattice-based algorithm in the mining time.


Transfer of semantics from argumentation frameworks to logic programming A preliminary report

Abstract: There are various interesting semantics' (extensions) designed for argumentation frameworks. They enable to assign a meaning, e.g., to odd-length cycles. Our main motivation is to transfer semantics' proposed by Baroni, Giacomin and Guida for argumetation frameworks with odd-length cycles to logic programs with odd-length cycles through default negation. The developed construction is even stronger. For a given logic program an argumentation framework is defined. The construction enables to transfer each semantics of the resulting argumentation framework to a semantics of the given logic program. Weak points of the construction are discussed and some future continuations of this approach are outlined.


The expressibility of functions on the Boolean domain, with applications to Counting CSPs

Abstract: An important tool in the study of the complexity of Constraint Satisfaction Problems (CSPs) is the notion of a relational clone, which is the set of all relations expressible using primitive positive formulas over a particular set of base relations. Post's lattice gives a complete classification of all Boolean relational clones, and this has been used to classify the computational difficulty of CSPs. Motivated by a desire to understand the computational complexity of (weighted) counting CSPs, we develop an analogous notion of functional clones and study the landscape of these clones. One of these clones is the collection of log-supermodular (lsm) functions, which turns out to play a significant role in classifying counting CSPs. In the conservative case (where all nonnegative unary functions are available), we show that there are no functional clones lying strictly between the clone of lsm functions and the total clone (containing all functions). Thus, any counting CSP that contains a single nontrivial non-lsm function is computationally as hard to approximate as any problem in #P. Furthermore, we show that any non-trivial functional clone (in a sense that will be made precise) contains the binary function "implies". As a consequence, in the conservative case, all non-trivial counting CSPs are as hard as #BIS, the problem of counting independent sets in a bipartite graph. Given the complexity-theoretic results, it is natural to ask whether the "implies" clone is equivalent to the clone of lsm functions. We use the Mobius transform and the Fourier transform to show that these clones coincide precisely up to arity 3. It is an intriguing open question whether the lsm clone is finitely generated. Finally, we investigate functional clones in which only restricted classes of unary functions are available.


Checking Finite State Machine Conformance when there are Distributed Observations

Abstract: This paper concerns state-based systems that interact with their environment at physically distributed interfaces, called ports. When such a system is used a projection of the global trace, called a local trace, is observed at each port. This leads to the environment having reduced observational power: the set of local traces observed need not uniquely define the global trace that occurred. We consider the previously defined implementation relation $\sqsubseteq_s$ and start by investigating the problem of defining a language ${\mathcal {\tilde L}} (M)$ for a multi-port finite state machine (FSM) $M$ such that $N \sqsubseteq_s M$ if and only if every global trace of $N$ is in ${\mathcal {\tilde L}} (M)$. The motivation is that if we can produce such a language ${\mathcal {\tilde L}} (M)$ then this can potentially be used to inform development and testing. We show that ${\mathcal {\tilde L}} (M)$ can be uniquely defined but need not be regular. We then prove that it is generally undecidable whether $N \sqsubseteq_s M$, a consequence of this result being that it is undecidable whether there is a test case that is capable of distinguishing two states or two multi-port FSM in distributed testing. This result complements a previous result that it is undecidable whether there is a test case that is guaranteed to distinguish two states or multi-port FSMs. We also give some conditions under which $N \sqsubseteq_s M$ is decidable. We then consider the implementation relation $\sqsubseteq_s^k$ that only concerns input sequences of length $k$ or less. Naturally, given FSMs $N$ and $M$ it is decidable whether $N \sqsubseteq_s^k M$ since only a finite set of traces is relevant. We prove that if we place bounds on $k$ and the number of ports then we can decide $N \sqsubseteq_s^k M$ in polynomial time but otherwise this problem is NP-hard.


Solving Principal Component Pursuit in Linear Time via $l_1$ Filtering

Abstract: In the past decades, exactly recovering the intrinsic data structure from corrupted observations, which is known as robust principal component analysis (RPCA), has attracted tremendous interests and found many applications in computer vision. Recently, this problem has been formulated as recovering a low-rank component and a sparse component from the observed data matrix. It is proved that under some suitable conditions, this problem can be exactly solved by principal component pursuit (PCP), i.e., minimizing a combination of nuclear norm and $l_1$ norm. Most of the existing methods for solving PCP require singular value decompositions (SVD) of the data matrix, resulting in a high computational complexity, hence preventing the applications of RPCA to very large scale computer vision problems. In this paper, we propose a novel algorithm, called $l_1$ filtering, for \emph{exactly} solving PCP with an $O(r^2(m+n))$ complexity, where $m\times n$ is the size of data matrix and $r$ is the rank of the matrix to recover, which is supposed to be much smaller than $m$ and $n$. Moreover, $l_1$ filtering is \emph{highly parallelizable}. It is the first algorithm that can \emph{exactly} solve a nuclear norm minimization problem in \emph{linear time} (with respect to the data size). Experiments on both synthetic data and real applications testify to the great advantage of $l_1$ filtering in speed over state-of-the-art algorithms.


Confluent Hasse diagrams

Abstract: We show that a transitively reduced digraph has a confluent upward drawing if and only if its reachability relation has order dimension at most two. In this case, we construct a confluent upward drawing with $O(n^2)$ features, in an $O(n) \times O(n)$ grid in $O(n^2)$ time. For the digraphs representing series-parallel partial orders we show how to construct a drawing with $O(n)$ features in an $O(n) \times O(n)$ grid in $O(n)$ time from a series-parallel decomposition of the partial order. Our drawings are optimal in the number of confluent junctions they use.


Linear Time Inference of Strings from Cover Arrays using a Binary Alphabet

Abstract: Covers being one of the most popular form of regularities in strings, have drawn much attention over time. In this paper, we focus on the problem of linear time inference of strings from cover arrays using the least sized alphabet possible. We present an algorithm that can reconstruct a string $x$ over a two-letter alphabet whenever a valid cover array $C$ is given as an input. This algorithm uses several interesting combinatorial properties of cover arrays and an interesting relation between border array and cover array to achieve this. Our algorithm runs in linear time.


Probabilistic Performance Analysis of Networks using an Improved Network Service Envelope Approach

Abstract: Stochastic network calculus is an evolving theory which accounts for statistical multiplexing and uses an envelope approach for probabilistic delay and backlog analysis of networks. One of the key ideas of stochastic network calculus is the possibility to describe service offered at network node as a stochastic service envelope, which in turn can be used to describe the stochastic service available in a network of nodes and determine end-to-end probabilistic delay and backlog bounds. This paper introduces a new definition of stochastic service envelopes which yield a simple network service envelope and tighter end-to-end performance bounds. It is shown for ($\sigma(\theta), \rho(\theta)$) - constrained traffic model that the end-to-end performance measures computed using the new stochastic network service envelope are tight in comparison to the ones obtained using the existing start-of-the-art definition of statistical network service envelope and are bounded by ${\cal O}(H \log{H})$, where $H$ is the number of nodes traversed by the arrival traffic.


Unit Testing in ASPIDE

Abstract: Answer Set Programming (ASP) is a declarative logic programming formalism, which is employed nowadays in both academic and industrial real-world applications. Although some tools for supporting the development of ASP programs have been proposed in the last few years, the crucial task of testing ASP programs received less attention, and is an Achilles' heel of the available programming environments. In this paper we present a language for specifying and running unit tests on ASP programs. The testing language has been implemented in ASPIDE, a comprehensive IDE for ASP, which supports the entire life-cycle of ASP development with a collection of user-friendly graphical tools for program composition, testing, debugging, profiling, solver execution configuration, and output-handling.


A Uniform Fixpoint Approach to the Implementation of Inference Methods for Deductive Databases

Abstract: Within the research area of deductive databases three different database tasks have been deeply investigated: query evaluation, update propagation and view updating. Over the last thirty years various inference mechanisms have been proposed for realizing these main functionalities of a rule-based system. However, these inference mechanisms have been rarely used in commercial DB systems until now. One important reason for this is the lack of a uniform approach well-suited for implementation in an SQL-based system. In this paper, we present such a uniform approach in form of a new version of the soft consequence operator. Additionally, we present improved transformation-based approaches to query optimization and update propagation and view updating which are all using this operator as underlying evaluation mechanism.


Deciding first order logic properties of matroids

Abstract: Frick and Grohe [J. ACM 48 (2006), 1184-1206] introduced a notion of graph classes with locally bounded tree-width and established that every first order logic property can be decided in almost linear time in such a graph class. Here, we introduce an analogous notion for matroids (locally bounded branch-width) and show the existence of a fixed parameter algorithm for first order logic properties in classes of regular matroids with locally bounded branch-width. To obtain this result, we show that the problem of deciding the existence of a circuit of length at most k containing two given elements is fixed parameter tractable for regular matroids.


Personalized Web Services for Web Information Extraction

Abstract: The field of information extraction from the Web emerged with the growth of the Web and the multiplication of online data sources. This paper is an analysis of information extraction methods. It presents a service oriented approach for web information extraction considering both web data management and extraction services. Then we propose an SOA based architecture to enhance flexibility and on-the-fly modification of web extraction services. An implementation of the proposed architecture is proposed on the middleware level of Java Enterprise Edition (JEE) servers.


Quality Evaluation of Conceptual Level Object Multidimensional Data Model

Abstract: The advancement of technology facilitates explosive growth of mobile usage in the last decade. Numerous applications have been developed to support its usage. However, gap in technology exists in obtaining correct and trusted values for evaluation indexes of the precise amount of usage. The claims of loss in revenue by the service providers could be more due to unexpected behaviour of the hardware. A similar mistrust is often observed in the users of the services. A trustworthy subscription scheme is in demand for consumers whereas revenue needs to be assured of the service providers. Multiple Authorizations by Multiple Owners (MAMO) has already been introduced as a technology to build trust in the third party billing system. In this paper, MAMO is extended to ensure trustworthiness of the parameters for subscription. Along with call transaction data are reconciled to assure the proper revenue generation.


New Results on the Fault-Tolerant Facility Placement Problem

Abstract: We studied the Fault-Tolerant Facility Placement problem (FTFP) which generalizes the uncapacitated facility location problem (UFL). In FTFP, we are given a set F of sites at which facilities can be built, and a set C of clients with some demands that need to be satisfied by different facilities. A client $j$ has demand $r_j$. Building one facility at a site $i$ incurs a cost $f_i$, and connecting one unit of demand from client $j$ to a facility at site $i\in\fac$ costs $d_{ij}$. $d_{ij}$'s are assumed to form a metric. A feasible solution specifies the number of facilities to be built at each site and the way to connect demands from clients to facilities, with the restriction that demands from the same client must go to different facilities. Facilities at the same site are considered different. The goal is to find a solution with minimum total cost. We gave a 1.7245-approximation algorithm to the FTFP problem. Our technique is via a reduction to the Fault-Tolerant Facility Location problem, in which each client has demand $r_j$ but each site can have at most one facility built.


Distributed Power Control and Coding-Modulation Adaptation in Wireless Networks using Annealed Gibbs Sampling

Abstract: In wireless networks, the transmission rate of a link is determined by received signal strength, interference from simultaneous transmissions, and available coding-modulation schemes. Rate allocation is a key problem in wireless network design, but a very challenging problem because: (i) wireless interference is global, i.e., a transmission interferes all other simultaneous transmissions, and (ii) the rate-power relation is non-convex and non-continuous, where the discontinuity is due to limited number of coding-modulation choices in practical systems. In this paper, we propose a distributed power control and coding-modulation adaptation algorithm using annealed Gibbs sampling, which achieves throughput optimality in an arbitrary network topology. We consider a realistic Signal-to-Interference-and-Noise-Ratio (SINR) based interference model, and assume continuous power space and finite rate options (coding-modulation choices). Our algorithm first decomposes network-wide interference to local interference by properly choosing a "neighborhood" for each transmitter and bounding the interference from non-neighbor nodes. The power update policy is then carefully designed to emulate a Gibbs sampler over a Markov chain with a continuous state space. We further exploit the technique of simulated annealing to speed up the convergence of the algorithm to the optimal power and coding-modulation configuration. Finally, simulation results demonstrate the superior performance of the proposed algorithm.


Green Cellular Networks: A Survey, Some Research Issues and Challenges

Abstract: Energy efficiency in cellular networks is a growing concern for cellular operators to not only maintain profitability, but also to reduce the overall environment effects. This emerging trend of achieving energy efficiency in cellular networks is motivating the standardization authorities and network operators to continuously explore future technologies in order to bring improvements in the entire network infrastructure. In this article, we present a brief survey of methods to improve the power efficiency of cellular networks, explore some research issues and challenges and suggest some techniques to enable an energy efficient or "green" cellular network. Since base stations consume a maximum portion of the total energy used in a cellular system, we will first provide a comprehensive survey on techniques to obtain energy savings in base stations. Next, we discuss how heterogeneous network deployment based on micro, pico and femto-cells can be used to achieve this goal. Since cognitive radio and cooperative relaying are undisputed future technologies in this regard, we propose a research vision to make these technologies more energy efficient. Lastly, we explore some broader perspectives in realizing a "green" cellular network technology


Geographic Trough Filling for Internet Datacenters

Abstract: To reduce datacenter energy consumption and cost, current practice has considered demand-proportional resource provisioning schemes, where servers are turned on/off according to the load of requests. Most existing work considers instantaneous (Internet) requests only, which are explicitly or implicitly assumed to be delay-sensitive. On the other hand, in datacenters, there exist a vast amount of delay-tolerant jobs, such as background/maintainance jobs. In this paper, we explicitly differentiate delay-sensitive jobs and delay tolerant jobs. We focus on the problem of using delay-tolerant jobs to fill the extra capacity of datacenters, referred to as trough/valley filling. Giving a higher priority to delay-sensitive jobs, our schemes complement to most existing demand-proportional resource provisioning schemes. Our goal is to design intelligent trough filling mechanisms that are energy efficient and also achieve good delay performance. Specifically, we propose two joint dynamic speed scaling and traffic shifting schemes, one subgradient-based and the other queue-based. Our schemes assume little statistical information of the system, which is usually difficult to obtain in practice. In both schemes, energy cost saving comes from dynamic speed scaling, statistical multiplexing, electricity price diversity, and service efficiency diversity. In addition, good delay performance is achieved in the queue-based scheme via load shifting and capacity allocation based on queue conditions. Practical issues that may arise in datacenter networks are considered, including capacity and bandwidth constraint, service agility constraint, and load shifting cost. We use both artificial and real datacenter traces to evaluate the proposed schemes.


Facile Algebraic Representation of a Novel Quaternary Logic

Abstract: In this work, a novel quaternary algebra has been proposed that can be used to implement an arbitrary quaternary logic function in more than one systematic ways. The proposed logic has evolved from and is closely related to the Boolean algebra for binary domain; yet it does not lack the benefits of a higher-radix system. It offers seamless integration of the binary logic functions and expressions through a set of transforms and allows any binary logic simplification technique to be applied in quaternary domain. Since physical realization of the operators defined in this logic has recently been reported, it has become very important to have a well-defined algebra that will facilitate the algebraic manipulation of the novel quaternary logic and aid in designing various complex logic circuits. Therefore, based on our earlier works, here we describe the complete algebraic representation of this logic for the first time. The efficacy of the logic has been shown by designing and comparing several common logic circuits with existing designs in both binary and quaternary domain.


A Note on Separable Nonlinear Least Squares Problem

Abstract: Separable nonlinear least squares (SNLS)problem is a special class of nonlinear least squares (NLS)problems, whose objective function is a mixture of linear and nonlinear functions. It has many applications in many different areas, especially in Operations Research and Computer Sciences. They are difficult to solve with the infinite-norm metric. In this paper, we give a short note on the separable nonlinear least squares problem, unseparated scheme for NLS, and propose an algorithm for solving mixed linear-nonlinear minimization problem, method of which results in solving a series of least squares separable problems.


Strategic Learning and Robust Protocol Design for Online Communities with Selfish Users

Abstract: This paper focuses on analyzing the free-riding behavior of self-interested users in online communities. Hence, traditional optimization methods for communities composed of compliant users such as network utility maximization cannot be applied here. In our prior work, we show how social reciprocation protocols can be designed in online communities which have populations consisting of a continuum of users and are stationary under stochastic permutations. Under these assumptions, we are able to prove that users voluntarily comply with the pre-determined social norms and cooperate with other users in the community by providing their services. In this paper, we generalize the study by analyzing the interactions of self-interested users in online communities with finite populations and are not stationary. To optimize their long-term performance based on their knowledge, users adapt their strategies to play their best response by solving individual stochastic control problems. The best-response dynamic introduces a stochastic dynamic process in the community, in which the strategies of users evolve over time. We then investigate the long-term evolution of a community, and prove that the community will converge to stochastically stable equilibria which are stable against stochastic permutations. Understanding the evolution of a community provides protocol designers with guidelines for designing social norms in which no user has incentives to adapt its strategy and deviate from the prescribed protocol, thereby ensuring that the adopted protocol will enable the community to achieve the optimal social welfare.


The update complexity of selection and related problems

Abstract: We present a framework for computing with input data specified by intervals, representing uncertainty in the values of the input parameters. To compute a solution, the algorithm can query the input parameters that yield more refined estimates in form of sub-intervals and the objective is to minimize the number of queries. The previous approaches address the scenario where every query returns an exact value. Our framework is more general as it can deal with a wider variety of inputs and query responses and we establish interesting relationships between them that have not been investigated previously. Although some of the approaches of the previous restricted models can be adapted to the more general model, we require more sophisticated techniques for the analysis and we also obtain improved algorithms for the previous model. We address selection problems in the generalized model and show that there exist 2-update competitive algorithms that do not depend on the lengths or distribution of the sub-intervals and hold against the worst case adversary. We also obtain similar bounds on the competitive ratio for the MST problem in graphs.


Multi-Robot Organisms: State of the Art

Abstract: This paper represents the state of the art development on the field of artificial multi-robot organisms. It briefly considers mechatronic development, sensor and computational equipment, software framework and introduces one of the Grand Challenges for swarm and reconfigurable robotics.


Parsing Combinatory Categorial Grammar with Answer Set Programming: Preliminary Report

Abstract: Combinatory categorial grammar (CCG) is a grammar formalism used for natural language parsing. CCG assigns structured lexical categories to words and uses a small set of combinatory rules to combine these categories to parse a sentence. In this work we propose and implement a new approach to CCG parsing that relies on a prominent knowledge representation formalism, answer set programming (ASP) - a declarative programming paradigm. We formulate the task of CCG parsing as a planning problem and use an ASP computational tool to compute solutions that correspond to valid parses. Compared to other approaches, there is no need to implement a specific parsing algorithm using such a declarative method. Our approach aims at producing all semantically distinct parse trees for a given sentence. From this goal, normalization and efficiency issues arise, and we deal with them by combining and extending existing strategies. We have implemented a CCG parsing tool kit - AspCcgTk - that uses ASP as its main computational means. The C&C supertagger can be used as a preprocessor within AspCcgTk, which allows us to achieve wide-coverage natural language parsing.


FdConfig: A Constraint-Based Interactive Product Configurator

Abstract: We present a constraint-based approach to interactive product configuration. Our configurator tool FdConfig is based on feature models for the representation of the product domain. Such models can be directly mapped into constraint satisfaction problems and dealt with by appropriate constraint solvers. During the interactive configuration process the user generates new constraints as a result of his configuration decisions and even may retract constraints posted earlier. We discuss the configuration process, explain the underlying techniques and show optimizations.


A Performance Study of Data Mining Techniques: Multiple Linear Regression vs. Factor Analysis

Abstract: The growing volume of data usually creates an interesting challenge for the need of data analysis tools that discover regularities in these data. Data mining has emerged as disciplines that contribute tools for data analysis, discovery of hidden knowledge, and autonomous decision making in many application domains. The purpose of this study is to compare the performance of two data mining techniques viz., factor analysis and multiple linear regression for different sample sizes on three unique sets of data. The performance of the two data mining techniques is compared on following parameters like mean square error (MSE), R-square, R-Square adjusted, condition number, root mean square error(RMSE), number of variables included in the prediction model, modified coefficient of efficiency, F-value, and test of normality. These parameters have been computed using various data mining tools like SPSS, XLstat, Stata, and MS-Excel. It is seen that for all the given dataset, factor analysis outperform multiple linear regression. But the absolute value of prediction accuracy varied between the three datasets indicating that the data distribution and data characteristics play a major role in choosing the correct prediction technique.


Implementing Equational Constraints in a Functional Language

Abstract: KiCS2 is a new system to compile functional logic programs of the source language Curry into purely functional Haskell programs. The implementation is based on the idea to represent the search space as a data structure and logic variables as operations that generate their values. This has the advantage that one can apply various, and in particular, complete search strategies to compute solutions. However, the generation of all values for logic variables might be inefficient for applications that exploit constraints on partially known values. To overcome this drawback, we propose new techniques to implement equational constraints in this framework. In particular, we show how unification modulo function evaluation and functional patterns can be added without sacrificing the efficiency of the kernel implementation.


Modification of GTD from Flat File Format to OLAP for Data Mining

Abstract: This document is part of original research work by the authors in a bid to explore new fields for applying Data Mining Techniques. The sample data is part of a large data set from University of Maryland (UMD) and outlines how more meaningful patterns can be discovered by preprocessing the data in the form of OLAP cubes.


Multi-Robot Searching Algorithm Using Levy Flight and Artificial Potential Field

Abstract: An efficient search algorithm is very crucial in robotic area, especially for exploration missions, where the target availability is unknown and the condition of the environment is highly unpredictable. In a very large environment, it is not sufficient to scan an area or volume by a single robot, multiple robots should be involved to perform the collective exploration. In this paper, we propose to combine bio-inspired search algorithm called Levy flight and artificial potential field method to perform an efficient searching algorithm for multi-robot applications. The main focus of this work is not only to prove the concept or to measure the efficiency of the algorithm by experiments, but also to develop an appropriate generic framework to be implemented both in simulation and on real robotic platforms. Several experiments, which compare different search algorithms, are also performed.


Nested HEX-Programs

Abstract: Answer-Set Programming (ASP) is an established declarative programming paradigm. However, classical ASP lacks subprogram calls as in procedural programming, and access to external computations (like remote procedure calls) in general. The feature is desired for increasing modularity and---assuming proper access in place---(meta-)reasoning over subprogram results. While HEX-programs extend classical ASP with external source access, they do not support calls of (sub-)programs upfront. We present nested HEX-programs, which extend HEX-programs to serve the desired feature, in a user-friendly manner. Notably, the answer sets of called sub-programs can be individually accessed. This is particularly useful for applications that need to reason over answer sets like belief set merging, user-defined aggregate functions, or preferences of answer sets.


Collective Adaptive Systems: Challenges Beyond Evolvability

Abstract: This position paper overviews several challenges of collective adaptive systems, which are beyond the research objectives of current top-projects in ICT, and especially in FET, initiatives. The attention is paid not only to challenges and new research topics, but also to their impact and potential breakthroughs in information and communication technologies.


A prototype of a knowledge-based programming environment

Abstract: In this paper we present a proposal for a knowledge-based programming environment. In such an environment, declarative background knowledge, procedures, and concrete data are represented in suitable languages and combined in a flexible manner. This leads to a highly declarative programming style. We illustrate our approach on an example and report about our prototype implementation.


Datum-Wise Classification: A Sequential Approach to Sparsity

Abstract: We propose a novel classification technique whose aim is to select an appropriate representation for each datapoint, in contrast to the usual approach of selecting a representation encompassing the whole dataset. This datum-wise representation is found by using a sparsity inducing empirical risk, which is a relaxation of the standard L 0 regularized risk. The classification problem is modeled as a sequential decision process that sequentially chooses, for each datapoint, which features to use before classifying. Datum-Wise Classification extends naturally to multi-class tasks, and we describe a specific case where our inference has equivalent complexity to a traditional linear classifier, while still using a variable number of features. We compare our classifier to classical L 1 regularized linear models (L 1-SVM and LARS) on a set of common binary and multi-class datasets and show that for an equal average number of features used we can get improved performance using our method.


Web Pages Clustering: A New Approach

Abstract: The rapid growth of web has resulted in vast volume of information. Information availability at a rapid speed to the user is vital. English language (or any for that matter) has lot of ambiguity in the usage of words. So there is no guarantee that a keyword based search engine will provide the required results. This paper introduces the use of dictionary (standardised) to obtain the context with which a keyword is used and in turn cluster the results based on this context. These ideas can be merged with a metasearch engine to enhance the search efficiency.


E- Learning: An effective pedagogical tool for learning

Abstract: In the info-tech age E-Methods of learning are becoming the most important vehicle in disseminating knowledge in higher education institutions. This sector is growing and changing at a rapid speed due to developments in technologies. But teaching is an art. Can there be fun learning with raw and dry technology? How can we make the best use of E- Methods, can we make the required information and data available to the students in a flexible manner, at ease all the time? What are the advantages of traditional methods of teaching and learning? Is E-learning a progressive stage incubating all the benefits of the Manual learning or it is only a window dressing on the face of advancement? Can we convert the boring, tedious subjects into interactive, monotony breaking joyous learning? In this paper the researchers have focused on the modernization of E- Pedagogy vis-a-vis the traditional method of learning. They have highlighted the effectiveness of using the E- learning elements and various E- Methods. This work has used the decision tree algorithms particularly Classifiers.trees.J48 The obtained results show that using online examination attribute plays major role in increasing the average grade of the class in higher education. The novelty of this work is that the researchers have focused on the teaching methodology used by the faculty members and the tools available in the universities. We believe that this work will play a constructive role in building higher education system. Our generated rules/output can be used by the decision makers in the improvement of higher education system processes.


Sliding Trellis-Based Frame Synchronization

Abstract: Frame Synchronization (FS) is required in several communication standards in order to recover the individual frames that have been aggregated in a burst. This paper proposes a low-delay and reducedcomplexity Sliding Trellis (ST)-based FS technique, compared to our previously proposed trellis-based FS method. Each burst is divided into overlapping windows in which FS is performed. Useful information is propagated from one window to the next. The proposed method makes use of soft information provided by the channel, but also of all sources of redundancy present in the protocol stack. An illustration of our STbased approach for the WiMAX Media Access Control (MAC) layer is provided. When FS is performed on bursts transmitted over Rayleigh fading channel, the ST-based approach reduces the FS latency and complexity at the cost of a very small performance degradation compared to our full complexity trellis-based FS and outperforms state-of-the-art FS techniques.


Consistent Reconstruction of the Input of an Oversampled Filter Bank From Noisy Subbands

Abstract: This paper introduces a reconstruction approach for the input signal of an oversampled filter bank (OFB) when the sub-bands generated at its output are quantized and transmitted over a noisy channel. This approach exploits the redundancy introduced by the OFB and the fact that the quantization noise is bounded. A maximum-likelihood estimate of the input signal is evaluated, which only considers the vectors of quantization indexes corresponding to subband signals that could have been generated by the OFB and that are compliant with the quantization errors. When considering an OFB with an oversampling ratio of 3/2 and a transmission of quantized subbands on an AWGN channel, compared to a classical decoder, the performance gains are up to 9 dB in terms of SNR for the reconstructed signal, and 3 dB in terms of channel SNR.


Generalized Fast Approximate Energy Minimization via Graph Cuts: Alpha-Expansion Beta-Shrink Moves

Abstract: We present alpha-expansion beta-shrink moves, a simple generalization of the widely-used alpha-beta swap and alpha-expansion algorithms for approximate energy minimization. We show that in a certain sense, these moves dominate both alpha-beta-swap and alpha-expansion moves, but unlike previous generalizations the new moves require no additional assumptions and are still solvable in polynomial-time. We show promising experimental results with the new moves, which we believe could be used in any context where alpha-expansions are currently employed.


On the enumerating series of an abstract numeration system

Abstract: It is known that any rational abstract numeration system is faithfully, and effectively, represented by an N-rational series. A simple proof of this result is given which yields a representation of this series which in turn allows a simple computation of the value of words in this system and easy constructions for the recognition of recognisable sets of numbers. It is also shown that conversely it is decidable whether an N-rational series corresponds to a rational abstract numeration system.


Structure Selection from Streaming Relational Data

Abstract: Statistical relational learning techniques have been successfully applied in a wide range of relational domains. In most of these applications, the human designers capitalized on their background knowledge by following a trial-and-error trajectory, where relational features are manually defined by a human engineer, parameters are learned for those features on the training data, the resulting model is validated, and the cycle repeats as the engineer adjusts the set of features. This paper seeks to streamline application development in large relational domains by introducing a light-weight approach that efficiently evaluates relational features on pieces of the relational graph that are streamed to it one at a time. We evaluate our approach on two social media tasks and demonstrate that it leads to more accurate models that are learned faster.


Optimal k-fold colorings of webs and antiwebs

Abstract: A k-fold x-coloring of a graph is an assignment of (at least) k distinct colors from the set {1, 2, ..., x} to each vertex such that any two adjacent vertices are assigned disjoint sets of colors. The smallest number x such that G admits a k-fold x-coloring is the k-th chromatic number of G, denoted by \chi_k(G). We determine the exact value of this parameter when G is a web or an antiweb. Our results generalize the known corresponding results for odd cycles and imply necessary and sufficient conditions under which \chi_k(G) attains its lower and upper bounds based on the clique, the fractional chromatic and the chromatic numbers. Additionally, we extend the concept of \chi-critical graphs to \chi_k-critical graphs. We identify the webs and antiwebs having this property, for every integer k <= 1.


Each normal logic program has a 2-valued Minimal Hypotheses semantics

Abstract: In this paper we explore a unifying approach --- that of hypotheses assumption --- as a means to provide a semantics for all Normal Logic Programs (NLPs), the Minimal Hypotheses (MH) semantics. This semantics takes a positive hypotheses assumption approach as a means to guarantee the desirable properties of model existence, relevance and cumulativity, and of generalizing the Stable Models in the process. To do so we first introduce the fundamental semantic concept of minimality of assumed positive hypotheses, define the MH semantics, and analyze the semantics' properties and applicability. Indeed, abductive Logic Programming can be conceptually captured by a strategy centered on the assumption of abducibles (or hypotheses). Likewise, the Argumentation perspective of Logic Programs also lends itself to an arguments (or hypotheses) assumption approach. Previous works on Abduction have depicted the atoms of default negated literals in NLPs as abducibles, i.e., assumable hypotheses. We take a complementary and more general view than these works to NLP semantics by employing positive hypotheses instead.


Food Redistribution as Optimization

Abstract: In this paper we study the simultaneous problems of food waste and hunger in the context of the possible solution of food (waste) rescue and redistribution. To this end, we develop an empirical model that can be used in Monte Carlo simulations to study the dynamics of the underlying problem. Our model's parameters are derived from a unique data set provided by a large food bank and food rescue organization in north central Colorado. We find that food supply is a non-parametric heavy-tailed process that is well-modeled with an extreme value peaks-over-threshold model. Although the underlying process is stochastic, the basic approach of food rescue and redistribution appears to be feasible both at small and large scales. The ultimate efficacy of this model is intimately tied to the rate at which food expires and hence the ability to preserve and quickly transport and redistribute food. The cost of the redistribution is tied to the number and density of participating suppliers, and costs can be reduced (and supply increased) simply by recruiting additional donors to participate. Our results show that with sufficient funding and manpower, a significant amount of food can be rescued from the waste stream and used to feed the hungry.


Probability Ranking in Vector Spaces

Abstract: The Probability Ranking Principle states that the document set with the highest values of probability of relevance optimizes information retrieval effectiveness given the probabilities are estimated as accurately as possible. The key point of the principle is the separation of the document set into two subsets with a given level of fallout and with the highest recall. The paper introduces the separation between two vector subspaces and shows that the separation yields a more effective performance than the optimal separation into subsets with the same available evidence, the performance being measured with recall and fallout. The result is proved mathematically and exemplified experimentally.


The $k$-error linear complexity distribution for $2^n$-periodic binary sequences

Abstract: The linear complexity and the $k$-error linear complexity of a sequence have been used as important security measures for key stream sequence strength in linear feedback shift register design. By studying the linear complexity of binary sequences with period $2^n$, one could convert the computation of $k$-error linear complexity into finding error sequences with minimal Hamming weight. Based on Games-Chan algorithm, the $k$-error linear complexity distribution of $2^n$-periodic binary sequences is investigated in this paper. First, for $k=2,3$, the complete counting functions on the $k$-error linear complexity of $2^n$-periodic balanced binary sequences (with linear complexity less than $2^n$) are characterized. Second, for $k=3,4$, the complete counting functions on the $k$-error linear complexity of $2^n$-periodic binary sequences with linear complexity $2^n$ are presented. Third, as a consequence of these results, the counting functions for the number of $2^n$-periodic binary sequences with the $k$-error linear complexity for $k = 2$ and 3 are obtained. Further more, an important result in a recent paper is proved to be not completely correct.


A Constraint Logic Programming Approach for Computing Ordinal Conditional Functions

Abstract: In order to give appropriate semantics to qualitative conditionals of the form "if A then normally B", ordinal conditional functions (OCFs) ranking the possible worlds according to their degree of plausibility can be used. An OCF accepting all conditionals of a knowledge base R can be characterized as the solution of a constraint satisfaction problem. We present a high-level, declarative approach using constraint logic programming techniques for solving this constraint satisfaction problem. In particular, the approach developed here supports the generation of all minimal solutions; these minimal solutions are of special interest as they provide a basis for model-based inference from R.


Hierarchical N-body simulations with auto-tuning for heterogeneous systems

Abstract: With the current hybridization of treecodes and FMMs, combined with auto-tuning capabilities on heterogeneous architectures, the flexibility of fast N-body methods has been greatly enhanced. These features are a requirement to developing a black-box software library for fast N-body algorithms on heterogeneous systems, which is our immediate goal.


Banded Householder representation of linear subspaces

Abstract: We show how to compactly represent any $n$-dimensional subspace of $R^m$ as a banded product of Householder reflections using $n(m - n)$ floating point numbers. This is optimal since these subspaces form a Grassmannian space $Gr_n(m)$ of dimension $n(m - n)$. The representation is stable and easy to compute: any matrix can be factored into the product of a banded Householder matrix and a square matrix using two to three QR decompositions.


Confidentiality-Preserving Data Publishing for Credulous Users by Extended Abduction

Abstract: Publishing private data on external servers incurs the problem of how to avoid unwanted disclosure of confidential data. We study a problem of confidentiality in extended disjunctive logic programs and show how it can be solved by extended abduction. In particular, we analyze how credulous non-monotonic reasoning affects confidentiality.


Translating Answer-Set Programs into Bit-Vector Logic

Abstract: Answer set programming (ASP) is a paradigm for declarative problem solving where problems are first formalized as rule sets, i.e., answer-set programs, in a uniform way and then solved by computing answer sets for programs. The satisfiability modulo theories (SMT) framework follows a similar modelling philosophy but the syntax is based on extensions of propositional logic rather than rules. Quite recently, a translation from answer-set programs into difference logic was provided---enabling the use of particular SMT solvers for the computation of answer sets. In this paper, the translation is revised for another SMT fragment, namely that based on fixed-width bit-vector theories. Thus, even further SMT solvers can be harnessed for the task of computing answer sets. The results of a preliminary experimental comparison are also reported. They suggest a level of performance which is similar to that achieved via difference logic.


Show Me Your Cookie And I Will Tell You Who You Are

Abstract: With the success of Web applications, most of our data is now stored on various third-party servers where they are processed to deliver personalized services. Naturally we must be authenticated to access this personal information, but the use of personalized services only restricted by identification could indirectly and silently leak sensitive data. We analyzed Google Web Search access mechanisms and found that the current policy applied to session cookies could be used to retrieve users' personal data. We describe an attack scheme leveraging the search personalization (based on the same SID cookie) to retrieve a part of the victim's click history and even some of her contacts. We implemented a proof of concept of this attack on Firefox and Chrome Web browsers and conducted an experiment with ten volunteers. Thanks to this prototype we were able to recover up to 80% of the user's search click history.


Designing Practical Distributed Exchange for Online Communities

Abstract: In many online systems, individuals provide services for each other; the recipient of the service obtains a benefit but the provider of the service incurs a cost. If benefit exceeds cost, provision of the service increases social welfare and should therefore be encouraged -- but the individuals providing the service gain no (immediate) benefit from providing the service and hence have an incentive to withhold service. Hence there is scope for designing a system that improves welfare by encouraging exchange. To operate successfully within the confines of the online environment, such a system should be distributed, practicable, and consistent with individual incentives. This paper proposes and analyzes a simple such system that relies on the exchange of {\em tokens}; the emphasis is on the design of a protocol (number of tokens and suggested strategies). We provide estimates for the efficiency of such protocols and show that choosing the right protocol will lead to almost full efficiency if agents are sufficiently patient. However, choosing the wrong protocols may lead to an enormous loss of efficiency.


Edge-preserving self-healing: keeping network backbones densely connected

Abstract: Healing algorithms play a crucial part in distributed P2P networks where failures occur continuously and frequently. Several self-healing algorithms have been suggested recently [IPDPS'08, PODC'08, PODC'09, PODC'11] in a line of work that has yielded gradual improvements in the properties ensured on the graph. This work motivates a strong general phenomenon of edge-preserving healing that aims at obtaining self-healing algorithms with the constraint that all original edges in the graph (not deleted by the adversary), be retained in every intermediate graph. The previous algorithms, in their nascent form, are not explicitly edge preserving. In this paper, we show they can be suitably modified (We introduce Xheal+, an edge-preserving version of Xheal[PODC'11]). Towards this end, we present a general self-healing model that unifies the previous models. The main contribution of this paper is not in the technical complexity, rather in the simplicity with which the edge-preserving property can be ensured and the message that this is a crucial property with several benefits. In particular, we highlight this by showing that, almost as an immediate corollary, subgraph densities are preserved or increased. Maintaining density is a notion motivated by the fact that in certain distributed networks, certain nodes may require and initially have a larger number of inter-connections. It is vital that a healing algorithm, even amidst failures, respect these requirements. Our suggested modifications yield such subgraph density preservation as a by product. In addition, edge preservation helps maintain any subgraph induced property that is monotonic. Also, algorithms that are edge-preserving require minimal alteration of edges which can be an expensive cost in healing - something that has not been modeled in any of the past work.


Information Dissemination in Unknown Radio networks with Large Labels

Abstract: We consider the problems of deterministic broadcasting and gossiping in completely unknown ad-hoc radio networks. We assume that nothing is known to the nodes about the topology or even the size of the network, $n$, except that $n > 1$. Protocols for vanilla model, when $n$ is known, may be run for increasingly larger estimates $2^i$ on the size of the network, but one cannot determine when such a protocol should terminate. Thus, to carry this design paradigm, successful completion or in-completion of the process should be detected, and this knowledge circulated in the network. We consider the problem of deterministic Acknowledged Broadcasting and Gossiping when nodes can take polynomially large labels. For the above setting, we present the following results for strongly connected networks: (a) A deterministic protocol for acknowledged broadcasting which takes $NRG(n,n^c)$ rounds, where $NRG(n,n^c)$ is the round complexity of deterministic gossiping for vanilla model. (b) A deterministic protocol for acknowledged gossiping, which takes $O(n^2 \lg n)$ rounds when collision detection mechanism is available. The structure of the transmissions of nodes in the network, to enable them to infer collisions, and discover existence of unknown in-neighborhood as a result, is abstracted as a family of integral sets called Selecting-Colliding family. We prove the existence of Selecting-Colliding families using the probabilistic method and employ them to design protocol for acknowledged gossiping when no collision detection mechanism is available. Finally, we present a deterministic protocol for acknowledged broadcasting for bidirectional networks, with a round complexity of $O(n \lg n)$ rounds.


Proof System for Plan Verification under 0-Approximation Semantics

Abstract: In this paper a proof system is developed for plan verification problems $\{X\}c\{Y\}$ and $\{X\}c\{KW p\}$ under 0-approximation semantics for ${\mathcal A}_K$. Here, for a plan $c$, two sets $X,Y$ of fluent literals, and a literal $p$, $\{X\}c\{Y\}$ (resp. $\{X\}c\{KW p\}$) means that all literals of $Y$ become true (resp. $p$ becomes known) after executing $c$ in any initial state in which all literals in $X$ are true.Then, soundness and completeness are proved. The proof system allows verifying plans and generating plans as well.


An Oracle-based, Output-sensitive Algorithm for Projections of Resultant Polytopes

Abstract: We design an algorithm to compute the Newton polytope of the resultant, known as resultant polytope, or its orthogonal projection along a given direction. The resultant is fundamental in algebraic elimination, optimization, and geometric modeling. Our algorithm exactly computes vertex- and halfspace-representations of the polytope using an oracle producing resultant vertices in a given direction, thus avoiding walking on the polytope whose dimension is alpha-n-1, where the input consists of alpha points in Z^n. Our approach is output-sensitive as it makes one oracle call per vertex and facet. It extends to any polytope whose oracle-based definition is advantageous, such as the secondary and discriminant polytopes. Our publicly available implementation uses the experimental CGAL package triangulation. Our method computes 5-, 6- and 7-dimensional polytopes with 35K, 23K and 500 vertices, respectively, within 2hrs, and the Newton polytopes of many important surface equations encountered in geometric modeling in <1sec, whereas the corresponding secondary polytopes are intractable. It is faster than tropical geometry software up to dimension 5 or 6. Hashing determinantal predicates accelerates execution up to 100 times. One variant computes inner and outer approximations with, respectively, 90% and 105% of the true volume, up to 25 times faster.


Domain-specific Languages in a Finite Domain Constraint Programming System

Abstract: In this paper, we present domain-specific languages (DSLs) that we devised for their use in the implementation of a finite domain constraint programming system, available as library(clpfd) in SWI-Prolog and YAP-Prolog. These DSLs are used in propagator selection and constraint reification. In these areas, they lead to concise specifications that are easy to read and reason about. At compilation time, these specifications are translated to Prolog code, reducing interpretative run-time overheads. The devised languages can be used in the implementation of other finite domain constraint solvers as well and may contribute to their correctness, conciseness and efficiency.


A State-of-the-art Survey on IDS for Mobile Ad-Hoc Networks and Wireless Mesh Networks

Abstract: An Intrusion Detection System (IDS) detects malicious and selfish nodes in a network. Ad hoc networks are often secured by using either intrusion detection or by secure routing. Designing efficient IDS for wireless ad-hoc networks that would not affect the performance of the network significantly is indeed a challenging task. Arguably, the most common thing in a review paper in the domain of wireless networks is to compare the performances of different solutions using simulation results. However, variance in multiple configuration aspects including that due to different underlying routing protocols, makes the task of simulation based comparative evaluation of IDS solutions somewhat unrealistic. In stead, the authors have followed an analytic approach to identify the gaps in the existing IDS solutions for MANETs and wireless mesh networks. The paper aims to ease the job of a new researcher by exposing him to the state of the art research issues on IDS. Nearly 80% of the works cited in this paper are published with in last 3 to 4 years.


Improving Entity Resolution with Global Constraints

Abstract: Some of the greatest advances in web search have come from leveraging socio-economic properties of online user behavior. Past advances include PageRank, anchor text, hubs-authorities, and TF-IDF. In this paper, we investigate another socio-economic property that, to our knowledge, has not yet been exploited: sites that create lists of entities, such as IMDB and Netflix, have an incentive to avoid gratuitous duplicates. We leverage this property to resolve entities across the different web sites, and find that we can obtain substantial improvements in resolution accuracy. This improvement in accuracy also translates into robustness, which often reduces the amount of training data that must be labeled for comparing entities across many sites. Furthermore, the technique provides robustness when resolving sites that have some duplicates, even without first removing these duplicates. We present algorithms with very strong precision and recall, and show that max weight matching, while appearing to be a natural choice turns out to have poor performance in some situations. The presented techniques are now being used in the back-end entity resolution system at a major Internet search engine.


Balancing Degree, Diameter and Weight in Euclidean Spanners

Abstract: In this paper we devise a novel \emph{unified} construction of Euclidean spanners that trades between the maximum degree, diameter and weight gracefully. For a positive integer k, our construction provides a (1+eps)-spanner with maximum degree O(k), diameter O(log_k n + alpha(k)), weight O(k \cdot log_k n \cdot log n) \cdot w(MST(S)), and O(n) edges. Note that for k= n^{1/alpha(n)} this gives rise to diameter O(alpha(n)), weight O(n^{1/alpha(n)} \cdot log n \cdot alpha(n)) \cdot w(MST(S)) and maximum degree O(n^{1/alpha(n)}), which improves upon a classical result of Arya et al. \cite{ADMSS95}; in the corresponding result from \cite{ADMSS95} the spanner has the same number of edges and diameter, but its weight and degree may be arbitrarily large. Also, for k = O(1) this gives rise to maximum degree O(1), diameter O(log n) and weight O(log^2 n) \cdot w(MST(S)), which reproves another classical result of Arya et al. \cite{ADMSS95}. Our bound of O(log_k n + alpha(k)) on the diameter is optimal under the constraints that the maximum degree is O(k) and the number of edges is O(n). Our bound on the weight is optimal up to a factor of log n. Our construction also provides a similar tradeoff in the complementary range of parameters, i.e., when the weight should be smaller than log^2 n, but the diameter is allowed to grow beyond log n. For random point sets in the d-dimensional unit cube, we "shave" a factor of log n from the weight bound. Specifically, in this case our construction achieves maximum degree O(k), diameter O(log_k n + alpha(k)) and weight that is with high probability O(k \cdot log_k n) \cdot w(MST(S)). Finally, en route to these results we devise optimal constructions of 1-spanners for general tree metrics, which are of independent interest.


Multi-Unit Auction with Diminishing Marginal Valuations and Capacities

Abstract: This paper has been withdrawn by the author.


Optimizing Network Topology to Reduce Aggregate Traffic in Systems of Mobile Robots

Abstract: Systems of networked mobile robots, such as unmanned aerial or ground vehicles, will play important roles in future military and commercial applications. The communications for such systems will typically be over wireless links and may require that the robots form an ad hoc network and communicate on a peer-to-peer basis. In this paper, we consider the problem of optimizing the network topology to minimize the total traffic in a network required to support a given set of data flows under constraints on the amount of movement possible at each mobile robot. In this paper, we consider a subclass of this problem in which the initial and final topologies are trees, and the movement restrictions are given in terms of the number of edges in the graph that must be traversed. We develop algorithms to optimize the network topology while maintaining network connectivity during the topology reconfiguration process. Our topology reconfiguration algorithm uses the concept of prefix labelling and routing to move nodes through the network while maintaining network connectivity. We develop two algorithms to determine the final network topology: an optimal, but computationally complex algorithm, and a greedy suboptimal algorithm that has much lower complexity. We present simulation results to compare the performance of these algorithm.


No Internal Regret via Neighborhood Watch

Abstract: We present an algorithm which attains O(\sqrt{T}) internal (and thus external) regret for finite games with partial monitoring under the local observability condition. Recently, this condition has been shown by (Bartok, Pal, and Szepesvari, 2011) to imply the O(\sqrt{T}) rate for partial monitoring games against an i.i.d. opponent, and the authors conjectured that the same holds for non-stochastic adversaries. Our result is in the affirmative, and it completes the characterization of possible rates for finite partial-monitoring games, an open question stated by (Cesa-Bianchi, Lugosi, and Stoltz, 2006). Our regret guarantees also hold for the more general model of partial monitoring with random signals.


Proceedings Second Workshop on Logics for Component Configuration

Abstract: This volume contains the papers presented at the second international workshop on Logics for Component Configuration (LoCoCo 2011) which was associated with the International Conference on Principles and Practice of Constraint Programming (CP 2011) and which took place on September 12, 2011 in Perugia, Italy. Representing and solving configuration problems is a hot topic of great importance for many application domains. For example, modern software distributions are based on the notion of components, which denote units of independent development and deployment. Components provide the necessary flexibility when organizing a complex software distribution, but also are a challenge when it comes to selecting components from a large repository of possible choices, and configuring these components according to user needs, resource constraints, and interdependencies with other components. Some well-known examples of complex systems of components in the world of Free and Open Source software are the different distributions for GNU/Linux, BSD, or Eclipse plugins. The LoCoCo workshop focus on logic-based methods for specifying and solving complex configuration problems. The goal of the workshop was to bring together both researchers and practitioners active in the area of component configuration of systems, using different modeling and solving techniques, such as constraint and logic programming, description logics, satisfiability and its extensions. The workshop was an opportunity to discuss common and complementary solutions for solving component configuration.


Private Decayed Sum Estimation under Continual Observation

Abstract: In monitoring applications, recent data is more important than distant data. How does this affect privacy of data analysis? We study a general class of data analyses - computing predicate sums - with privacy. Formally, we study the problem of estimating predicate sums {\em privately}, for sliding windows (and other well-known decay models of data, i.e. exponential and polynomial decay). We extend the recently proposed continual privacy model of Dwork et al. We present algorithms for decayed sum which are $\eps$-differentially private, and are accurate. For window and exponential decay sums, our algorithms are accurate up to additive $1/\eps$ and polylog terms in the range of the computed function; for polynomial decay sums which are technically more challenging because partial solutions do not compose easily, our algorithms incur additional relative error. Further, we show lower bounds, tight within polylog factors and tight with respect to the dependence on the probability of error.