Multi-level Contextual Type Theory

Abstract: Contextual type theory distinguishes between bound variables and meta-variables to write potentially incomplete terms in the presence of binders. It has found good use as a framework for concise explanations of higher-order unification, characterize holes in proofs, and in developing a foundation for programming with higher-order abstract syntax, as embodied by the programming and reasoning environment Beluga. However, to reason about these applications, we need to introduce meta^2-variables to characterize the dependency on meta-variables and bound variables. In other words, we must go beyond a two-level system granting only bound variables and meta-variables. In this paper we generalize contextual type theory to n levels for arbitrary n, so as to obtain a formal system offering bound variables, meta-variables and so on all the way to meta^n-variables. We obtain a uniform account by collapsing all these different kinds of variables into a single notion of variabe indexed by some level k. We give a decidable bi-directional type system which characterizes beta-eta-normal forms together with a generalized substitution operation.


Nominal Logic with Equations Only

Abstract: Many formal systems, particularly in computer science, may be captured by equations modulated by side conditions asserting the "freshness of names"; these can be reasoned about with Nominal Equational Logic (NEL). Like most logics of this sort NEL employs this notion of freshness as a first class logical connective. However, this can become inconvenient when attempting to translate results from standard equational logic to the nominal setting. This paper presents proof rules for a logic whose only connectives are equations, which we call Nominal Equation-only Logic (NEoL). We prove that NEoL is just as expressive as NEL. We then give a simple description of equality in the empty NEoL-theory, then extend that result to describe freshness in the empty NEL-theory.


Nominal Henkin Semantics: simply-typed lambda-calculus models in nominal sets

Abstract: We investigate a class of nominal algebraic Henkin-style models for the simply typed lambda-calculus in which variables map to names in the denotation and lambda-abstraction maps to a (non-functional) name-abstraction operation. The resulting denotations are smaller and better-behaved, in ways we make precise, than functional valuation-based models. Using these new models, we then develop a generalisation of \lambda-term syntax enriching them with existential meta-variables, thus yielding a theory of incomplete functions. This incompleteness is orthogonal to the usual notion of incompleteness given by function abstraction and application, and corresponds to holes and incomplete objects.


An Improved Implementation and Abstract Interface for Hybrid

Abstract: Hybrid is a formal theory implemented in Isabelle/HOL that provides an interface for representing and reasoning about object languages using higher-order abstract syntax (HOAS). This interface is built around an HOAS variable-binding operator that is constructed definitionally from a de Bruijn index representation. In this paper we make a variety of improvements to Hybrid, culminating in an abstract interface that on one hand makes Hybrid a more mathematically satisfactory theory, and on the other hand has important practical benefits. We start with a modification of Hybrid's type of terms that better hides its implementation in terms of de Bruijn indices, by excluding at the type level terms with dangling indices. We present an improved set of definitions, and a series of new lemmas that provide a complete characterization of Hybrid's primitives in terms of properties stated at the HOAS level. Benefits of this new package include a new proof of adequacy and improvements to reasoning about object logics. Such proofs are carried out at the higher level with no involvement of the lower level de Bruijn syntax.


Proof-irrelevant model of CC with predicative induction and judgmental equality

Abstract: We present a set-theoretic, proof-irrelevant model for Calculus of Constructions (CC) with predicative induction and judgmental equality in Zermelo-Fraenkel set theory with an axiom for countably many inaccessible cardinals. We use Aczel's trace encoding which is universally defined for any function type, regardless of being impredicative. Direct and concrete interpretations of simultaneous induction and mutually recursive functions are also provided by extending Dybjer's interpretations on the basis of Aczel's rule sets. Our model can be regarded as a higher-order generalization of the truth-table methods. We provide a relatively simple consistency proof of type theory, which can be used as the basis for a theorem prover.


A secure additive protocol for card players

Abstract: Consider three players Alice, Bob and Cath who hold a, b and c cards, respectively, from a deck of d=a+b+c cards. The cards are all different and players only know their own cards. Suppose Alice and Bob wish to communicate their cards to each other without Cath learning whether Alice or Bob holds a specific card. Considering the cards as consecutive natural numbers 0,1,..., we investigate general conditions for when Alice or Bob can safely announce the sum of the cards they hold modulo an appropriately chosen integer. We demonstrate that this holds whenever a,b>2 and c=1. Because Cath holds a single card, this also implies that Alice and Bob will learn the card deal from the other player's announcement.


Applying Fuzzy ID3 Decision Tree for Software Effort Estimation

Abstract: Web Effort Estimation is a process of predicting the efforts and cost in terms of money, schedule and staff for any software project system. Many estimation models have been proposed over the last three decades and it is believed that it is a must for the purpose of: Budgeting, risk analysis, project planning and control, and project improvement investment analysis. In this paper, we investigate the use of Fuzzy ID3 decision tree for software cost estimation; it is designed by integrating the principles of ID3 decision tree and the fuzzy set-theoretic concepts, enabling the model to handle uncertain and imprecise data when describing the software projects, which can improve greatly the accuracy of obtained estimates. MMRE and Pred are used as measures of prediction accuracy for this study. A series of experiments is reported using two different software projects datasets namely, Tukutuku and COCOMO'81 datasets. The results are compared with those produced by the crisp version of the ID3 decision tree.


Classification of extremal and $s$-extremal binary self-dual codes of length 38

Abstract: In this paper we classify all extremal and $s$-extremal binary self-dual codes of length 38. There are exactly 2744 extremal $[38,19,8]$ self-dual codes, two $s$-extremal $[38,19,6]$ codes, and 1730 $s$-extremal $[38,19,8]$ codes. We obtain our results from the use of a recursive algorithm used in the recent classification of all extremal self-dual codes of length 36, and from a generalization of this recursive algorithm for the shadow. The classification of $s$-extremal $[38,19,6]$ codes permits to achieve the classification of all $s$-extremal codes with d=6.


Gas turbine diagnostic system

Abstract: The creation of the systems models is very actual at present time, because it allow to simulate the work of some complex equipment without any additional spends. The given model of gas turbine is allowed to test and optimize the software for gas turbine automation systems, study station personal, like operators and engineers and will be useful for diagnostics and prediction tasks to analyze the efficiency of the gas turbine.


Construction of an NP Problem with an Exponential Lower Bound

Abstract: In this paper we present a Hashed-Path Traveling Salesperson Problem (HPTSP), a new type of problem which has the interesting property of having no polynomial time solutions. Next we show that HPTSP is in the class NP by demonstrating that local information about sub-routes is insufficient to compute the complete value of each route. As a consequence, via Ladner's theorem, we show that the class NPI is non-empty.


Anonymous Meeting in Networks

Abstract: A team consisting of an unknown number of mobile agents, starting from different nodes of an unknown network, possibly at different times, have to meet at the same node. Agents are anonymous (identical), execute the same deterministic algorithm and move in synchronous rounds along links of the network. Which configurations are gatherable and how to gather all of them deterministically by the same algorithm? We give a complete solution of this gathering problem in arbitrary networks. We characterize all gatherable configurations and give two universal deterministic gathering algorithms, i.e., algorithms that gather all gatherable configurations. The first algorithm works under the assumption that an upper bound n on the size of the network is known. In this case our algorithm guarantees gathering with detection, i.e., the existence of a round for any gatherable configuration, such that all agents are at the same node and all declare that gathering is accomplished. If no upper bound on the size of the network is known, we show that a universal algorithm for gathering with detection does not exist. Hence, for this harder scenario, we construct a second universal gathering algorithm, which guarantees that, for any gatherable configuration, all agents eventually get to one node and stop, although they cannot tell if gathering is over. The time of the first algorithm is polynomial in the upper bound n on the size of the network, and the time of the second algorithm is polynomial in the (unknown) size itself. Our results have an important consequence for the leader election problem for anonymous agents in arbitrary graphs. For anonymous agents in graphs, leader election turns out to be equivalent to gathering with detection. Hence, as a by-product, we obtain a complete solution of the leader election problem for anonymous agents in arbitrary graphs.


OpenWeather: a peer-to-peer weather data transmission protocol

Abstract: The study of the weather is performed using instruments termed weather stations. These weather stations are distributed around the world, collecting the data from the different phenomena. Several weather organizations have been deploying thousands of these instruments, creating big networks to collect weather data. These instruments are collecting the weather data and delivering it for later processing in the collections points. Nevertheless, all the methodologies used to transmit the weather data are based in protocols non adapted for this purpose. Thus, the weather stations are limited by the data formats and protocols used in them, not taking advantage of the real-time data available on them. We research the weather instruments, their technology and their network capabilities, in order to provide a solution for the mentioned problem. OpenWeather is the protocol proposed to provide a more optimum and reliable way to transmit the weather data. We evaluate the environmental factors, such as location or bandwidth availability, in order to design a protocol adapted to the requirements established by the automatic weather stations. A peer to peer architecture is proposed, providing a functional implementation of OpenWeather protocol. The evaluation of the protocol is executed in a real scenario, providing the hints to adapt the protocol to a common automatic weather station.


Exploring Linkablility of Community Reviewing

Abstract: Large numbers of people all over the world read and contribute to various review sites. Many contributors are understandably concerned about privacy; specifically, about linkability of reviews (and accounts) across review sites. In this paper, we study linkability of community reviewing and try to answer the question: to what extent are "anonymous" reviews linkable, i.e., likely authored by the same contributor? Based on a very large set of reviews from a popular site (Yelp), we show that a high percentage of ostensibly anonymous reviews can be linked with very high confidence. This is despite the fact that we use very simple models and equally simple features set. Our study suggests that contributors reliably expose their identities in reviews. This has important implications for cross-referencing accounts between different review sites. Also, techniques used in our study could be adopted by review sites to give contributors feedback about privacy of their reviews.


Platform Dependent Verification: On Engineering Verification Tools for 21st Century

Abstract: The paper overviews recent developments in platform-dependent explicit-state LTL model checking.


Variations on Multi-Core Nested Depth-First Search

Abstract: Recently, two new parallel algorithms for on-the-fly model checking of LTL properties were presented at the same conference: Automated Technology for Verification and Analysis, 2011. Both approaches extend Swarmed NDFS, which runs several sequential NDFS instances in parallel. While parallel random search already speeds up detection of bugs, the workers must share some global information in order to speed up full verification of correct models. The two algorithms differ considerably in the global information shared between workers, and in the way they synchronize. Here, we provide a thorough experimental comparison between the two algorithms, by measuring the runtime of their implementations on a multi-core machine. Both algorithms were implemented in the same framework of the model checker LTSmin, using similar optimizations, and have been subjected to the full BEEM model database. Because both algorithms have complementary advantages, we constructed an algorithm that combines both ideas. This combination clearly has an improved speedup. We also compare the results with the alternative parallel algorithm for accepting cycle detection OWCTY-MAP. Finally, we study a simple statistical model for input models that do contain accepting cycles. The goal is to distinguish the speedup due to parallel random search from the speedup that can be attributed to clever work sharing schemes.


Distributed Parametric and Statistical Model Checking

Abstract: Statistical Model Checking (SMC) is a trade-off between testing and formal verification. The core idea of the approach is to conduct some simulations of the system and verify if they satisfy some given property. In this paper we show that SMC is easily parallelizable on a master/slaves architecture by introducing a series of algorithms that scale almost linearly with respect to the number of slave computers. Our approach has been implemented in the UPPAAL SMC toolset and applied on non-trivial case studies.


Lazy Decomposition for Distributed Decision Procedures

Abstract: The increasing popularity of automated tools for software and hardware verification puts ever increasing demands on the underlying decision procedures. This paper presents a framework for distributed decision procedures (for first-order problems) based on Craig interpolation. Formulas are distributed in a lazy fashion, i.e., without the use of costly decomposition algorithms. Potential models which are shown to be incorrect are reconciled through the use of Craig interpolants. Experimental results on challenging propositional satisfiability problems indicate that our method is able to outperform traditional solving techniques even without the use of additional resources.


PKind: A parallel k-induction based model checker

Abstract: PKind is a novel parallel k-induction-based model checker of invariant properties for finite- or infinite-state Lustre programs. Its architecture, which is strictly message-based, is designed to minimize synchronization delays and easily accommodate the incorporation of incremental invariant generators to enhance basic k-induction. We describe PKind's functionality and main features, and present experimental evidence that PKind significantly speeds up the verification of safety properties and, due to incremental invariant generation, also considerably increases the number of provable ones.


CoInDiVinE: Parallel Distributed Model Checker for Component-Based Systems

Abstract: CoInDiVinE is a tool for parallel distributed model checking of interactions among components in hierarchical component-based systems. The tool extends the DiVinE framework with a new input language (component-interaction automata) and a property specification logic (CI-LTL). As the language differs from the input language of DiVinE, our tool employs a new state space generation algorithm that also supports partial order reduction. Experiments indicate that the tool has good scaling properties when run in parallel setting.


Distributed MAP in the SpinJa Model Checker

Abstract: Spin in Java (SpinJa) is an explicit state model checker for the Promela modelling language also used by the SPIN model checker. Designed to be extensible and reusable, the implementation of SpinJa follows a layered approach in which each new layer extends the functionality of the previous one. While SpinJa has preliminary support for shared-memory model checking, it did not yet support distributed-memory model checking. This tool paper presents a distributed implementation of a maximal accepting predecessors (MAP) search algorithm on top of SpinJa.


The HIVE Tool for Informed Swarm State Space Exploration

Abstract: Swarm verification and parallel randomised depth-first search are very effective parallel techniques to hunt bugs in large state spaces. In case bugs are absent, however, scalability of the parallelisation is completely lost. In recent work, we proposed a mechanism to inform the workers which parts of the state space to explore. This mechanism is compatible with any action-based formalism, where a state space can be represented by a labelled transition system. With this extension, each worker can be strictly bounded to explore only a small fraction of the state space at a time. In this paper, we present the HIVE tool together with two search algorithms which were added to the LTSmin tool suite to both perform a preprocessing step, and execute a bounded worker search. The new tool is used to coordinate informed swarm explorations, and the two new LTSmin algorithms are employed for preprocessing a model and performing the individual searches.


Outlier Detection for DNA Fragment Assembly

Abstract: Given $n$ length-$\ell$ strings $S =\{s_1, ..., s_n\}$ over a constant size alphabet $\Sigma$ together with parameters $d$ and $k$, the objective in the {\em Consensus String with Outliers} problem is to find a subset $S^*$ of $S$ of size $n-k$ and a string $s$ such that $\sum_{s_i \in S^*} d(s_i, s) \leq d$. Here $d(x, y)$ denotes the Hamming distance between the two strings $x$ and $y$. We prove 1. a variant of {\em Consensus String with Outliers} where the number of outliers $k$ is fixed and the objective is to minimize the total distance $\sum_{s_i \in S^*} d(s_i, s)$ admits a simple PTAS. (ii) Under the natural assumption that the number of outliers $k$ is small, the PTAS for the distance minimization version of {\em Consensus String with Outliers} performs well. In particular, as long as $k\leq cn$ for a fixed constant $c < 1$, the algorithm provides a $(1+\epsilon)$-approximate solution in time $f(1/\epsilon)(n\ell)^{O(1)}$ and thus, is an EPTAS. 2. In order to improve the PTAS for {\em Consensus String with Outliers} to an EPTAS, the assumption that $k$ is small is necessary. Specifically, when $k$ is allowed to be arbitrary the {\em Consensus String with Outliers} problem does not admit an EPTAS unless FPT=W[1]. This hardness result holds even for binary alphabets. 3. The decision version of {\em Consensus String with Outliers} is fixed parameter tractable when parameterized by $\frac{d}{n-k}$. and thus, also when parameterized by just $d$. To the best of our knowledge, {\em Consensus String with Outliers} is the first problem that admits a PTAS, and is fixed parameter tractable when parameterized by the value of the objective function but does not admit an EPTAS under plausible complexity assumptions.


An Efficient Security Mechanism for High-Integrity Wireless Sensor Networks

Abstract: Wireless sensor networks (WSNs) have recently attracted a lot of interest in the research community due their wide range of applications. Unfortunately, these networks are vulnerable to numerous security threats that can adversely affect their proper functioning. This problem is more critical if the network is deployed for some mission-critical applications such as in a tactical battlefield. Random failure of nodes and intentional compromise of nodes by an insider attack in a WSN pose particularly difficult challenges to security engineers as these attacks cannot be defended by traditional cryptography-based mechanisms. In this paper, a security solution is proposed for detecting compromised and faulty nodes in a WSN. The mechanism also isolates a compromised node from the network so that it cannot participate in any network activity. The proposed mechanism is based on misbehavior classification, behaviour monitoring and trust management. It involves minimum computation and communication overhead and is ideally suited for a resource-constrained, high-integrity WSN.


A Distributed Intrusion Detection System Using Cooperating Agents

Abstract: The current intrusion detection systems have a number of problems that limit their configurability, scalability and efficiency. There have been some propositions about distributed architectures based on multiple independent agents working collectively for intrusion detection. However, these distributed intrusion detection systems are not fully distributed as most of them centrally analyze data collected from distributed nodes which may lead to a single point of failure. In this paper, a distributed intrusion detection architecture is presented that is based on autonomous and cooperating agents without any centralized analysis components. The agents cooperate by using a hierarchical communication of interests and data, and the analysis of intrusion data is made by the agents at the lowest level of the hierarchy. This architecture provides significant advantages in scalability, flexibility, extensibility, fault tolerance, and resistance to compromise. A proof-of-concept prototype is developed and experiments have been conducted on it. The results show the effectiveness of the system in detecting intrusive activities.


A Distributed Protocol for Detection of Packet Dropping Attack in Mobile Ad Hoc Networks

Abstract: In multi-hop mobile ad hoc networks (MANETs),mobile nodes cooperate with each other without using any infrastructure such as access points or base stations. Security remains a major challenge for these networks due to their features of open medium, dynamically changing topologies, reliance on cooperative algorithms, absence of centralized monitoring points, and lack of clear lines of defense. Among the various attacks to which MANETs are vulnerable, malicious packet dropping attack is very common where a malicious node can partially degrade or completely disrupt communication in the network by consistently dropping packets. In this paper, a mechanism for detection of packet dropping attack is presented based on cooperative participation of the nodes in a MANET. The redundancy of routing information in an ad hoc network is utilized to make the scheme robust so that it works effectively even in presence of transient network partitioning and Byzantine failure of nodes. The proposed scheme is fully cooperative and thus more secure as the vulnerabilities of any election algorithm used for choosing a subset of nodes for cooperation are absent. Simulation results show the effectiveness of the protocol.


A Mechanism for Detection of Gray Hole Attack in Mobile Ad Hoc Networks

Abstract: Protecting the network layer from malicious attacks is an important and challenging security issue in mobile ad hoc networks (MANETs). In this paper, a security mechanism is proposed to defend against a cooperative gray hole attack on the well known AODV routing protocol in MANETs. A gray hole is a node that selectively drops and forwards data packets after it advertises itself as having the shortest path to the destination node in response to a route request message from a source node. The proposed mechanism does not apply any cryptographic primitives on the routing messages. Instead, it protects the network by detecting and reacting to malicious activities of any node. Simulation results show that the scheme has a significantly high detection rate with moderate network traffic overhead.


A Mechanism for Detection of Cooperative Black Hole Attack in Mobile Ad Hoc Networks

Abstract: A mobile ad hoc network (MANET) is a collection of autonomous nodes that communicate with each other by forming a multi-hop radio network and maintaining connections in a decentralized manner. Security remains a major challenge for these networks due to their features of open medium, dynamically changing topologies, reliance on cooperative algorithms,absence of centralized monitoring points, and lack of clear lines of defense. Most of the routing protocols for MANETs are thus vulnerable to various types of attacks. Ad hoc on-demand distance vector routing (AODV) is a very popular routing algorithm. However, it is vulnerable to the well-known black hole attack, where a malicious node falsely advertises good paths to a destination node during the route discovery process. This attack becomes more sever when a group of malicious nodes cooperate each other. In this paper, a defense mechanism is presented against a coordinated attack by multiple black hole nodes in a MANET. The simulation carried out on the proposed scheme has produced results that demonstrate the effectiveness of the mechanism in detection of the attack while maintaining a reasonable level of throughput in the network.


Making the long code shorter, with applications to the Unique Games Conjecture

Abstract: The long code is a central tool in hardness of approximation, especially in questions related to the unique games conjecture. We construct a new code that is exponentially more efficient, but can still be used in many of these applications. Using the new code we obtain exponential improvements over several known results, including the following: 1. For any eps > 0, we show the existence of an n vertex graph G where every set of o(n) vertices has expansion 1 - eps, but G's adjacency matrix has more than exp(log^delta n) eigenvalues larger than 1 - eps, where delta depends only on eps. This answers an open question of Arora, Barak and Steurer (FOCS 2010) who asked whether one can improve over the noise graph on the Boolean hypercube that has poly(log n) such eigenvalues. 2. A gadget that reduces unique games instances with linear constraints modulo K into instances with alphabet k with a blowup of K^polylog(K), improving over the previously known gadget with blowup of 2^K. 3. An n variable integrality gap for Unique Games that that survives exp(poly(log log n)) rounds of the SDP + Sherali Adams hierarchy, improving on the previously known bound of poly(log log n). We show a connection between the local testability of linear codes and small set expansion in certain related Cayley graphs, and use this connection to derandomize the noise graph on the Boolean hypercube.


Inclusion of Unambiguous RE#s is NP-Hard

Abstract: We show that testing inclusion between languages represented by regular expressions with numerical occurrence indicators (RE#s) is NP-hard, even if the expressions satisfy the requirement of "unambiguity", which is required for XML Schema content model expressions.


A Stochastic Net Model for Controlling Bullwhip Effect in Virtual Multi-Tier Retail Network

Abstract: Supply Chain operation is an integrated business process starting from primary supplier to end user and the process produce products, services and information. A successful chain will explore technology, lean operations, and quality management by adding value for customers and stakeholders. It is a strategic alliance among the partnering enterprises without geographical boundary. Every chain has its own unique set of market demands and operating challenges. Retailing is one such service domain of Supply Chain vulnerable to bullwhip effects. Demand uncertainty is one of the root causes of Bullwhip effects. This paper calls for modeling of a demand driven multi-tier stochastic Retail Chain to work against the Bullwhip effect. The proposed model of the operational chain will ensure significant return of share to the retailer through the sophisticated transaction management, real-time inventory management and the ability to track all inventory movements.


Approximate Stochastic Subgradient Estimation Training for Support Vector Machines

Abstract: Subgradient algorithms for training support vector machines have been quite successful for solving large-scale and online learning problems. However, they have been restricted to linear kernels and strongly convex formulations. This paper describes efficient subgradient approaches without such limitations. Our approaches make use of randomized low-dimensional approximations to nonlinear kernels, and minimization of a reduced primal formulation using an algorithm based on robust stochastic approximation, which do not require strong convexity. Experiments illustrate that our approaches produce solutions of comparable prediction accuracy with the solutions acquired from existing SVM solvers, but often in much shorter time. We also suggest efficient prediction schemes that depend only on the dimension of kernel approximation, not on the number of support vectors.


Pancake Flipping is Hard

Abstract: Pancake Flipping is the problem of sorting a stack of pancakes of different sizes (that is, a permutation), when the only allowed operation is to insert a spatula anywhere in the stack and to flip the pancakes above it (that is, to perform a prefix reversal). In the burnt variant, one side of each pancake is marked as burnt, and it is required to finish with all pancakes having the burnt side down. Computing the optimal scenario for any stack of pancakes and determining the worst-case stack for any stack size have been challenges over more than three decades. Beyond being an intriguing combinatorial problem in itself, it also yields applications, e.g. in parallel computing and computational biology. In this paper, we show that the Pancake Flipping problem, in its original (unburnt) variant, is NP-hard, thus answering the long-standing question of its computational complexity.


Kernel diff-hash

Abstract: This paper presents a kernel formulation of the recently introduced diff-hash algorithm for the construction of similarity-sensitive hash functions. Our kernel diff-hash algorithm that shows superior performance on the problem of image feature descriptor matching.


Equational theories of profinite structures

Abstract: In this paper we consider a general way of constructing profinite struc- tures based on a given framework - a countable family of objects and a countable family of recognisers (e.g. formulas). The main theorem states: A subset of a family of recognisable sets is a lattice if and only if it is definable by a family of profinite equations. This result extends Theorem 5.2 from [GGEP08] expressed only for finite words and morphisms to finite monoids. One of the applications of our theorem is the situation where objects are finite relational structures and recognisers are first order sentences. In that setting a simple characterisation of lattices of first order formulas arise.


Development of a Cost-efficient Autonomous MAV for an Unstructured Indoor Environment

Abstract: Performing rescuing and surveillance operations with autonomous ground and aerial vehicles become more and more apparent task. Involving unmanned robot systems allows making these operations more efficient, safe and reliable especially in hazardous areas. This work is devoted to the development of a cost-efficient micro aerial vehicle in a quadrocopter shape for developmental purposes within indoor scenarios. It has been constructed with off-the-shelf components available for mini helicopters. Additional sensors and electronics are incorporated into this aerial vehicle to stabilize its flight behavior and to provide a capability of an autonomous navigation in a partially unstructured indoor environment.


Spectrum Leasing as an Incentive towards Uplink Macrocell and Femtocell Cooperation

Abstract: The concept of femtocell access points underlaying existing communication infrastructure has recently emerged as a key technology that can significantly improve the coverage and performance of next-generation wireless networks. In this paper, we propose a framework for macrocell-femtocell cooperation under a closed access policy, in which a femtocell user may act as a relay for macrocell users. In return, each cooperative macrocell user grants the femtocell user a fraction of its superframe. We formulate a coalitional game with macrocell and femtocell users being the players, which can take individual and distributed decisions on whether to cooperate or not, while maximizing a utility function that captures the cooperative gains, in terms of throughput and delay.We show that the network can selforganize into a partition composed of disjoint coalitions which constitutes the recursive core of the game representing a key solution concept for coalition formation games in partition form. Simulation results show that the proposed coalition formation algorithm yields significant gains in terms of average rate per macrocell user, reaching up to 239%, relative to the non-cooperative case. Moreover, the proposed approach shows an improvement in terms of femtocell users' rate of up to 21% when compared to the traditional closed access policy.


On a Bounded Budget Network Creation Game

Abstract: We consider a network creation game in which each player (vertex) has a fixed budget to establish links to other players. In our model, each link has unit price and each agent tries to minimize its cost, which is either its local diameter or its total distance to other players in the (undirected) underlying graph of the created network. Two versions of the game are studied: in the MAX version, the cost incurred to a vertex is the maximum distance between the vertex and other vertices, and in the SUM version, the cost incurred to a vertex is the sum of distances between the vertex and other vertices. We prove that in both versions pure Nash equilibria exist, but the problem of finding the best response of a vertex is NP-hard. We take the social cost of the created network to be its diameter, and next we study the maximum possible diameter of an equilibrium graph with n vertices in various cases. When the sum of players' budgets is n-1, the equilibrium graphs are always trees, and we prove that their maximum diameter is Theta(n) and Theta(log n) in MAX and SUM versions, respectively. When each vertex has unit budget (i.e. can establish link to just one vertex), the diameter of any equilibrium graph in either version is Theta(1). We give examples of equilibrium graphs in the MAX version, such that all vertices have positive budgets and yet the diameter is Omega(sqrt(log n)). This interesting (and perhaps counter-intuitive) result shows that increasing the budgets may increase the diameter of equilibrium graphs and hence deteriorate the network structure. Then we prove that every equilibrium graph in the SUM version has diameter 2^O(sqrt(log n)). Finally, we show that if the budget of each player is at least k, then every equilibrium graph in the SUM version is k-connected or has diameter smaller than 4.


Model-driven system development: Experimental design and report of the pilot experiment

Abstract: This report describes de design of an experiment that intends to compare two variants of a modeldriven system development method, so as to assess the impact of requirements engineering practice in the quality of the conceptual models. The conceptual modelling method being assessed is the OO-Method [Pastor and Molina 2007]. One of its variants includes Communication Analysis, a communication-oriented requirements engineering method [Espa\~na, Gonz\'alez et al. 2009] and a set of guidelines to derive conceptual models from requirements models [Espa\~na, Ruiz et al. 2011; Gonz\'alez, Espa\~na et al. 2011]. The other variant is an ad-hoc, text-based requirements practice similar to the one that is applied in industrial projects by OO-Method practitioners. The goal of the research, summarised according to the Goal/Question/Metric template [Basili and Rombach 1988], is to: *) analyse the resulting models of two model-based information systems analysis method variants; namely, the OO-Method (OOM) and the integration of Communication Analysis and the OO-Method (CA+OOM), *) for the purpose of carrying out a comparative evaluation *) with respect to performance of the subject and acceptance of the method; *) from the viewpoint of the information systems researcher *) in the context of bachelor students.


Clique cover and graph separation: New incompressibility results

Abstract: The field of kernelization studies polynomial-time preprocessing routines for hard problems in the framework of parameterized complexity. Although a framework for proving kernelization lower bounds has been discovered in 2008 and successfully applied multiple times over the last three years, establishing kernelization complexity of many important problems remains open. In this paper we show that, unless NP is a subset of coNP/poly and the polynomial hierarchy collapses up to its third level, the following parameterized problems do not admit a polynomial-time preprocessing algorithm that reduces the size of an instance to polynomial in the parameter: - EDGE CLIQUE COVER, parameterized by the number of cliques, - DIRECTED EDGE/VERTEX MULTIWAY CUT, parameterized by the size of the cutset, even in the case of two terminals, - EDGE/VERTEX MULTICUT, parameterized by the size of the cutset, and - k-WAY CUT, parameterized by the size of the cutset. The existence of a polynomial kernelization for EDGE CLIQUE COVER was a seasoned veteran in open problem sessions. Furthermore, our results complement very recent developments in designing parameterized algorithms for cut problems by Marx and Razgon [STOC'11], Bousquet et al. [STOC'11], Kawarabayashi and Thorup [FOCS'11] and Chitnis et al. [SODA'12].


Information Spreading in Dynamic Graphs

Abstract: We present a general approach to study the flooding time (a measure of how fast information spreads) in dynamic graphs (graphs whose topology changes with time according to a random process). We consider arbitrary converging Markovian dynamic graph process, that is, processes in which the topology of the graph at time $t$ depends only on its topology at time $t-1$ and which have a unique stationary distribution. The most well studied models of dynamic graphs are all Markovian and converging. Under general conditions, we bound the flooding time in terms of the mixing time of the dynamic graph process. We recover, as special cases of our result, bounds on the flooding time for the \emph{random trip} model and the \emph{random path} models; previous analysis techniques provided bounds only in restricted settings for such models. Our result also provides the first bound for the \emph{random waypoint} model (which is tight for certain ranges of parameters) whose analysis had been an important open question.


Exploring Oracle RDBMS latches using Solaris DTrace

Abstract: Rise of hundreds cores technologies bring again to the first plan the problem of interprocess synchronization in database engines. Spinlocks are widely used in contemporary DBMS to synchronize processes at microsecond timescale. Latches are Oracle RDBMS specific spinlocks. The latch contention is common to observe in contemporary high concurrency OLTP environments. In contrast to system spinlocks used in operating systems kernels, latches work in user context. Such user level spinlocks are influenced by context preemption and multitasking. Until recently there were no direct methods to measure effectiveness of user spinlocks. This became possible with the emergence of Solaris 10 Dynamic Tracing framework. DTrace allows tracing and profiling both OS and user applications. This work investigates the possibilities to diagnose and tune Oracle latches. It explores the contemporary latch realization and spinning-blocking strategies, analyses corresponding statistic counters. A mathematical model developed to estimate analytically the effect of tuning _SPIN_COUNT value.


Beating Randomized Response on Incoherent Matrices

Abstract: Computing accurate low rank approximations of large matrices is a fundamental data mining task. In many applications however the matrix contains sensitive information about individuals. In such case we would like to release a low rank approximation that satisfies a strong privacy guarantee such as differential privacy. Unfortunately, to date the best known algorithm for this task that satisfies differential privacy is based on naive input perturbation or randomized response: Each entry of the matrix is perturbed independently by a sufficiently large random noise variable, a low rank approximation is then computed on the resulting matrix. We give (the first) significant improvements in accuracy over randomized response under the natural and necessary assumption that the matrix has low coherence. Our algorithm is also very efficient and finds a constant rank approximation of an m x n matrix in time O(mn). Note that even generating the noise matrix required for randomized response already requires time O(mn).


Computing Optimal Cycle Mean in Parallel on CUDA

Abstract: Computation of optimal cycle mean in a directed weighted graph has many applications in program analysis, performance verification in particular. In this paper we propose a data-parallel algorithmic solution to the problem and show how the computation of optimal cycle mean can be efficiently accelerated by means of CUDA technology. We show how the problem of computation of optimal cycle mean is decomposed into a sequence of data-parallel graph computation primitives and show how these primitives can be implemented and optimized for CUDA computation. Finally, we report a fivefold experimental speed up on graphs representing models of distributed systems when compared to best sequential algorithms.


Partial Redundancy Elimination for Multi-threaded Programs

Abstract: Multi-threaded programs have many applications which are widely used such as operating systems. Analyzing multi-threaded programs differs from sequential ones; the main feature is that many threads execute at the same time. The effect of all other running threads must be taken in account. Partial redundancy elimination is among the most powerful compiler optimizations: it performs loop-invariant code motion and common subexpression elimination. We present a type system with optimization component which performs partial redundancy elimination for multi-threaded programs.


Regular Functions, Cost Register Automata, and Generalized Min-Cost Problems

Abstract: Motivated by the successful application of the theory of regular languages to formal verification of finite-state systems, there is a renewed interest in developing a theory of analyzable functions from strings to numerical values that can provide a foundation for analyzing {\em quantitative} properties of finite-state systems. In this paper, we propose a deterministic model for associating costs with strings that is parameterized by operations of interest (such as addition, scaling, and $\min$), a notion of {\em regularity} that provides a yardstick to measure expressiveness, and study decision problems and theoretical properties of resulting classes of cost functions. Our definition of regularity relies on the theory of string-to-tree transducers, and allows associating costs with events that are conditional upon regular properties of future events. Our model of {\em cost register automata} allows computation of regular functions using multiple "write-only" registers whose values can be combined using the allowed set of operations. We show that classical shortest-path algorithms as well as algorithms designed for computing {\em discounted costs}, can be adopted for solving the min-cost problems for the more general classes of functions specified in our model. Cost register automata with $\min$ and increment give a deterministic model that is equivalent to {\em weighted automata}, an extensively studied nondeterministic model, and this connection results in new insights and new open problems.


Efficient Network for Non-Binary QC-LDPC Decoder

Abstract: This paper presents approaches to develop efficient network for non-binary quasi-cyclic LDPC (QC-LDPC) decoders. By exploiting the intrinsic shifting and symmetry properties of the check matrices, significant reduction of memory size and routing complexity can be achieved. Two different efficient network architectures for Class-I and Class-II non-binary QC-LDPC decoders have been proposed, respectively. Comparison results have shown that for the code of the 64-ary (1260, 630) rate-0.5 Class-I code, the proposed scheme can save more than 70.6% hardware required by shuffle network than the state-of-the-art designs. The proposed decoder example for the 32-ary (992, 496) rate-0.5 Class-II code can achieve a 93.8% shuffle network reduction compared with the conventional ones. Meanwhile, based on the similarity of Class-I and Class-II codes, similar shuffle network is further developed to incorporate both classes of codes at a very low cost.


Reduced-Latency SC Polar Decoder Architectures

Abstract: Polar codes have become one of the most favorable capacity achieving error correction codes (ECC) along with their simple encoding method. However, among the very few prior successive cancellation (SC) polar decoder designs, the required long code length makes the decoding latency high. In this paper, conventional decoding algorithm is transformed with look-ahead techniques. This reduces the decoding latency by 50%. With pipelining and parallel processing schemes, a parallel SC polar decoder is proposed. Sub-structure sharing approach is employed to design the merged processing element (PE). Moreover, inspired by the real FFT architecture, this paper presents a novel input generating circuit (ICG) block that can generate additional input signals for merged PEs on-the-fly. Gate-level analysis has demonstrated that the proposed design shows advantages of 50% decoding latency and twice throughput over the conventional one with similar hardware cost.


Low-Latency SC Decoder Architectures for Polar Codes

Abstract: Nowadays polar codes are becoming one of the most favorable capacity achieving error correction codes for their low encoding and decoding complexity. However, due to the large code length required by practical applications, the few existing successive cancellation (SC) decoder implementations still suffer from not only the high hardware cost but also the long decoding latency. This paper presents novel several approaches to design low-latency decoders for polar codes based on look-ahead techniques. Look-ahead techniques can be employed to reschedule the decoding process of polar decoder in numerous approaches. However, among those approaches, only well-arranged ones can achieve good performance in terms of both latency and hardware complexity. By revealing the recurrence property of SC decoding chart, the authors succeed in reducing the decoding latency by 50% with look-ahead techniques. With the help of VLSI-DSP design techniques such as pipelining, folding, unfolding, and parallel processing, methodologies for four different polar decoder architectures have been proposed to meet various application demands. Sub-structure sharing scheme has been adopted to design the merged processing element (PE) for further hardware reduction. In addition, systematic methods for construction refined pipelining decoder (2nd design) and the input generating circuits (ICG) block have been given. Detailed gate-level analysis has demonstrated that the proposed designs show latency advantages over conventional ones with similar hardware cost.


Maximum Bounded Rooted-Tree Packing Problem

Abstract: Given a graph and a root, the Maximum Bounded Rooted-Tree Packing (MBRTP) problem aims at finding K rooted-trees that span the largest subset of vertices, when each vertex has a limited outdegree. This problem is motivated by peer-to-peer streaming overlays in under-provisioned systems. We prove that the MBRTP problem is NP-complete. We present two polynomial-time algorithms that computes an optimal solution on complete graphs and trees respectively.


Online Learning with Preference Feedback

Abstract: We propose a new online learning model for learning with preference feedback. The model is especially suited for applications like web search and recommender systems, where preference data is readily available from implicit user feedback (e.g. clicks). In particular, at each time step a potentially structured object (e.g. a ranking) is presented to the user in response to a context (e.g. query), providing him or her with some unobserved amount of utility. As feedback the algorithm receives an improved object that would have provided higher utility. We propose a learning algorithm with provable regret bounds for this online learning setting and demonstrate its effectiveness on a web-search application. The new learning model also applies to many other interactive learning problems and admits several interesting extensions.


Generating Loop Invariants by Computing Vanishing Ideals of Sample Points

Abstract: Loop invariants play a very important role in proving correctness of programs. In this paper, we address the problem of generating invariants of polynomial loop programs. We present a new approach, for generating polynomial equation invariants of polynomial loop programs through computing vanishing ideals of sample points. We apply rational function interpolation, based on early termination technique, to generate invariants of loop programs with symbolic initial values. Our approach avoids first-order quantifier elimination and cylindrical algebraic decomposition(CAD). An algorithm for generating polynomial invariants is proposed and some examples are given to illustrate the algorithm. Furthermore, we demonstrate on a set of loop programs with symbolic initial values that our algorithm can yield polynomial invariants with degrees high up to 15.


Using Automated Dependency Analysis To Generate Representation Information

Abstract: To preserve access to digital content, we must preserve the representation information that captures the intended interpretation of the data. In particular, we must be able to capture performance dependency requirements, i.e. to identify the other resources that are required in order for the intended interpretation to be constructed successfully. Critically, we must identify the digital objects that are only referenced in the source data, but are embedded in the performance, such as fonts. This paper describes a new technique for analysing the dynamic dependencies of digital media, focussing on analysing the process that underlies the performance, rather than parsing and deconstructing the source data. This allows the results of format-specific characterisation tools to be verified independently, and facilitates the generation of representation information for any digital media format, even when no suitable characterisation tool exists.


Towards "Intelligent Compression" in Streams: A Biased Reservoir Sampling based Bloom Filter Approach

Abstract: With the explosion of information stored world-wide,data intensive computing has become a central area of research.Efficient management and processing of this massively exponential amount of data from diverse sources,such as telecommunication call data records,online transaction records,etc.,has become a necessity.Removing redundancy from such huge(multi-billion records) datasets resulting in resource and compute efficiency for downstream processing constitutes an important area of study. "Intelligent compression" or deduplication in streaming scenarios,for precise identification and elimination of duplicates from the unbounded datastream is a greater challenge given the realtime nature of data arrival.Stable Bloom Filters(SBF) address this problem to a certain extent.However,SBF suffers from a high false negative rate(FNR) and slow convergence rate,thereby rendering it inefficient for applications with low FNR tolerance.In this paper, we present a novel Reservoir Sampling based Bloom Filter,(RSBF) data structure,based on the combined concepts of reservoir sampling and Bloom filters for approximate detection of duplicates in data streams.Using detailed theoretical analysis we prove analytical bounds on its false positive rate(FPR),false negative rate(FNR) and convergence rates with low memory requirements.We show that RSBF offers the currently lowest FN and convergence rates,and are better than those of SBF while using the same memory.Using empirical analysis on real-world datasets(3 million records) and synthetic datasets with around 1 billion records,we demonstrate upto 2x improvement in FNR with better convergence rates as compared to SBF,while exhibiting comparable FPR.To the best of our knowledge,this is the first attempt to integrate reservoir sampling method with Bloom filters for deduplication in streaming scenarios.


Multidimensional Balanced Allocation for Multiple Choice & (1 + Beta) Processes

Abstract: Allocation of balls into bins is a well studied abstraction for load balancing problems.The literature hosts numerous results for sequential(single dimensional) allocation case when m balls are thrown into n bins. In this paper we study the symmetric multiple choice process for both unweighted and weighted balls as well as for both multidimensional and scalar models.Additionally,we present the results on bounds on gap for (1+beta) choice process with multidimensional balls and bins. We show that for the symmetric d choice process and with m=O(n), the upper bound on the gap is O(lnln(n)) w.h.p.This upper bound on the gap is within D=f factor of the lower bound. This is the first such tight result.For the general case of m>>n the expected gap is bounded by O(lnln(n)).For variable f and non-uniform distribution of the populated dimensions,we obtain the upper bound on the expected gap as O(log(n)). Further,for the multiple round parallel balls and bins,we show that the gap is also bounded by O(loglog(n)) for m=O(n).The same bound holds for the expected gap when m>>n. Our analysis also has strong implications in the sequential scalar case.For the weighted balls and bins and general case m>>n,we show that the upper bound on the expected gap is O(log(n)) which improves upon the best prior bound of n^c.Moreover,we show that for the (1 + beta) choice process and m=O(n) the upper bound(assuming uniform distribution of f populated dimensions over D total dimensions) on the gap is O(log(n)/beta),which is within D=f factor of the lower bound.For fixed f with non-uniform distribution and for random f with Binomial distribution the expected gap remains O(log(n)/beta) independent of the total number of balls thrown. This is the first such tight result for (1 +beta) paradigm with multidimensional balls and bins.


On the Value of Job Migration in Online Makespan Minimization

Abstract: Makespan minimization on identical parallel machines is a classical scheduling problem. We consider the online scenario where a sequence of $n$ jobs has to be scheduled non-preemptively on $m$ machines so as to minimize the maximum completion time of any job. The best competitive ratio that can be achieved by deterministic online algorithms is in the range $[1.88,1.9201]$. Currently no randomized online algorithm with a smaller competitiveness is known, for general $m$. In this paper we explore the power of job migration, i.e.\ an online scheduler is allowed to perform a limited number of job reassignments. Migration is a common technique used in theory and practice to balance load in parallel processing environments. As our main result we settle the performance that can be achieved by deterministic online algorithms. We develop an algorithm that is $\alpha_m$-competitive, for any $m\geq 2$, where $\alpha_m$ is the solution of a certain equation. For $m=2$, $\alpha_2 = 4/3$ and $\lim_{m\rightarrow \infty} \alpha_m = W_{-1}(-1/e^2)/(1+ W_{-1}(-1/e^2)) \approx 1.4659$. Here $W_{-1}$ is the lower branch of the Lambert $W$ function. For $m\geq 11$, the algorithm uses at most $7m$ migration operations. For smaller $m$, $8m$ to $10m$ operations may be performed. We complement this result by a matching lower bound: No online algorithm that uses $o(n)$ job migrations can achieve a competitive ratio smaller than $\alpha_m$. We finally trade performance for migrations. We give a family of algorithms that is $c$-competitive, for any $5/3\leq c \leq 2$. For $c= 5/3$, the strategy uses at most $4m$ job migrations. For $c=1.75$, at most $2.5m$ migrations are used.


Perfectly Balanced Allocation With Estimated Average Using Expected Constant Retries

Abstract: Balanced allocation of online balls-into-bins has long been an active area of research for efficient load balancing and hashing applications.There exists a large number of results in this domain for different settings, such as parallel allocations~\cite{parallel}, multi-dimensional allocations~\cite{multi}, weighted balls~\cite{weight} etc. For sequential multi-choice allocation, where $m$ balls are thrown into $n$ bins with each ball choosing $d$ (constant) bins independently uniformly at random, the maximum load of a bin is $O(\log \log n) + m/n$ with high probability~\cite{heavily_load}. This offers the current best known allocation scheme. However, for $d = \Theta(\log n)$, the gap reduces to $O(1)$~\cite{soda08}.A similar constant gap bound has been established for parallel allocations with $O(\log ^*n)$ communication rounds~\cite{lenzen}. In this paper we propose a novel multi-choice allocation algorithm, \emph{Improved D-choice with Estimated Average} ($IDEA$) achieving a constant gap with a high probability for the sequential single-dimensional online allocation problem with constant $d$. We achieve a maximum load of $\lceil m/n \rceil$ with high probability for constant $d$ choice scheme with \emph{expected} constant number of retries or rounds per ball. We also show that the bound holds even for an arbitrary large number of balls, $m>>n$. Further, we generalize this result to (i)~the weighted case, where balls have weights drawn from an arbitrary weight distribution with finite variance, (ii)~multi-dimensional setting, where balls have $D$ dimensions with $f$ randomly and uniformly chosen filled dimension for $m=n$, and (iii)~the parallel case, where $n$ balls arrive and are placed parallely in the bins. We show that the gap in these case is also a constant w.h.p. (independent of $m$) for constant value of $d$ with expected constant number of retries per ball.


Quantum/Relativistic Computation of Security and Efficiency of Electrical Power System for a Day-Ahead

Abstract: An algorithm for Electric Power System (EPS) quantum/relativistic security and efficiency computation for a day-ahead via perturbative renormalization of the EPS, finding the computation flowcharts, verification and validation is built in this paper.


OSERENA, an Optimized Coloring Algorithm for Dense or Large Scale Wireless Networks

Abstract: The goal of this research report is to present OSERENA "Optimized SchEduling RoutEr Node Activity", a distributed coloring algorithm optimized for dense wireless networks. Network density has an extremely reduced impact on the size of the messages exchanged to color the network. Furthermore, the number of colors used to color the network is not impacted by this optimization. We describe in this research report the properties of the algorithm and prove its correctness and termination. Simulation results point out the considerable gains in bandwidth.


Clause/Term Resolution and Learning in the Evaluation of Quantified Boolean Formulas

Abstract: Resolution is the rule of inference at the basis of most procedures for automated reasoning. In these procedures, the input formula is first translated into an equisatisfiable formula in conjunctive normal form (CNF) and then represented as a set of clauses. Deduction starts by inferring new clauses by resolution, and goes on until the empty clause is generated or satisfiability of the set of clauses is proven, e.g., because no new clauses can be generated. In this paper, we restrict our attention to the problem of evaluating Quantified Boolean Formulas (QBFs). In this setting, the above outlined deduction process is known to be sound and complete if given a formula in CNF and if a form of resolution, called Q-resolution, is used. We introduce Q-resolution on terms, to be used for formulas in disjunctive normal form. We show that the computation performed by most of the available procedures for QBFs --based on the Davis-Logemann-Loveland procedure (DLL) for propositional satisfiability-- corresponds to a tree in which Q-resolution on terms and clauses alternate. This poses the theoretical bases for the introduction of learning, corresponding to recording Q-resolution formulas associated with the nodes of the tree. We discuss the problems related to the introduction of learning in DLL based procedures, and present solutions extending state-of-the-art proposals coming from the literature on propositional satisfiability. Finally, we show that our DLL based solver extended with learning, performs significantly better on benchmarks used in the 2003 QBF solvers comparative evaluation.


Quantitative Languages Defined by Functional Automata

Abstract: A weighted automaton is functional if any two accepting runs on the same finite word have the same value. In this paper, we investigate functional weighted automata for four different measures: the sum, the mean, the discounted sum of weights along edges and the ratio between rewards and costs. On the positive side, we show that functionality is decidable for the four measures. Furthermore, the existential and universal threshold problems, the language inclusion problem and the equivalence problem are all decidable when the weighted automata are functional. On the negative side, we also study the quantitative extension of the realizability problem and show that it is undecidable for sum, mean and ratio. We finally show how to decide whether the language associated with a given functional automaton can be defined with a deterministic one, for sum, mean and discounted sum. The results on functionality and determinizability are expressed for the more general class of functional group automata. This allows one to formulate within the same framework new results related to discounted sum automata and known results on sum and mean automata. Ratio automata do not fit within this general scheme and different techniques are required to decide functionality.


A Formal Approach for Agent Based Large Concurrent Intelligent Systems

Abstract: Large Intelligent Systems are so complex these days that an urgent need for designing such systems in best available way is evolving. Modeling is the useful technique to show a complex real world system into the form of abstraction, so that analysis and implementation of the intelligent system become easy and is useful in gathering the prior knowledge of system that is not possible to experiment with the real world complex systems. This paper discusses a formal approach of agent-based large systems modeling for intelligent systems, which describes design level precautions, challenges and techniques using autonomous agents, as its fundamental modeling abstraction. We are discussing Ad-Hoc Network System as a case study in which we are using mobile agents where nodes are free to relocate, as they form an Intelligent Systems. The designing is very critical in this scenario and it can reduce the whole cost, time duration and risk involved in the project.


Collective Energy Foraging of Robot Swarms and Robot Organisms

Abstract: Cooperation and competition among stand-alone swarm agents increase collective fitness of the whole system. A principally new kind of collective systems is demonstrated by some bacteria and fungi, when they build symbiotic organisms. Symbiotic life forms emerge new functional and self-developmental capabilities, which allow better survival of swarm agents in different environments. In this paper we consider energy foraging scenario for two robotic species, swarm robots and symbiotic robot organism. It is indicated that aggregation of microrobots into a robot organism can provide better functional fitness for the whole group. A prototype of microrobots capable of autonomous aggregation and disaggregation are shown.


Game Theoretic Iterative Partitioning for Dynamic Load Balancing in Distributed Network Simulation

Abstract: High fidelity simulation of large-sized complex networks can be realized on a distributed computing platform that leverages the combined resources of multiple processors or machines. In a discrete event driven simulation, the assignment of logical processes (LPs) to machines is a critical step that affects the computational and communication burden on the machines, which in turn affects the simulation execution time of the experiment. We study a network partitioning game wherein each node (LP) acts as a selfish player. We derive two local node-level cost frameworks which are feasible in the sense that the aggregate state information required to be exchanged between the machines is independent of the size of the simulated network model. For both cost frameworks, we prove the existence of stable Nash equilibria in pure strategies. Using iterative partition improvements, we propose game theoretic partitioning algorithms based on the two cost criteria and show that each descends in a global cost. To exploit the distributed nature of the system, the algorithm is distributed, with each node's decision based on its local information and on a few global quantities which can be communicated machine-to-machine. We demonstrate the performance of our partitioning algorithm on an optimistic discrete event driven simulation platform that models an actual parallel simulator.


Using Neighborhood Beyond One Hop in Disruption-Tolerant Networks

Abstract: Most disruption-tolerant networking (DTN) protocols available in the literature have focused on mere contact and intercontact characteristics to make forwarding decisions. Nevertheless, there is a world behind contacts: just because one node is not in contact with some potential destination, it does not mean that this node is alone. There may be interesting end-to-end transmission opportunities through other nearby nodes. Existing protocols miss such possibilities by maintaining a simple contact-based view of the network. In this paper, we investigate how the vicinity of a node evolves through time and whether such information can be useful when routing data. We observe a clear tradeoff between routing performance and the cost for monitoring the neighborhood. Our analyses suggest that limiting a node's neighborhood view to three or four hops is more than enough to significantly improve forwarding efficiency without incurring prohibitive overhead.


Graph Regularized Nonnegative Matrix Factorization for Hyperspectral Data Unmixing

Abstract: Spectral unmixing is an important tool in hyperspectral data analysis for estimating endmembers and abundance fractions in a mixed pixel. This paper examines the applicability of a recently developed algorithm called graph regularized nonnegative matrix factorization (GNMF) for this aim. The proposed approach exploits the intrinsic geometrical structure of the data besides considering positivity and full additivity constraints. Simulated data based on the measured spectral signatures, is used for evaluating the proposed algorithm. Results in terms of abundance angle distance (AAD) and spectral angle distance (SAD) show that this method can effectively unmix hyperspectral data.


Active Property Testing

Abstract: One of the motivations for property testing of boolean functions is the idea that testing can serve as a preprocessing step before learning. However, in most machine learning applications, it is not possible to request for labels of fictitious examples constructed by the algorithm. Instead, the dominant query paradigm in applied machine learning, called active learning, is one where the algorithm may query for labels, but only on points in a given polynomial-sized (unlabeled) sample, drawn from some underlying distribution D. In this work, we bring this well-studied model in learning to the domain of testing. We show that for a number of important properties, testing can still yield substantial benefits in this setting. This includes testing unions of intervals, testing linear separators, and testing various assumptions used in semi-supervised learning. In addition to these specific results, we also develop a general notion of the testing dimension of a given property with respect to a given distribution. We show this dimension characterizes (up to constant factors) the intrinsic number of label requests needed to test that property. We develop such notions for both the active and passive testing models. We then use these dimensions to prove a number of lower bounds, including for linear separators and the class of dictator functions. Our results show that testing can be a powerful tool in realistic models for learning, and further that active testing exhibits an interesting and rich structure. Our work in addition brings together tools from a range of areas including U-statistics, noise-sensitivity, self-correction, and spectral analysis of random matrices, and develops new tools that may be of independent interest.


Towards Analyzing Crossover Operators in Evolutionary Search via General Markov Chain Switching Theorem

Abstract: Evolutionary algorithms (EAs), simulating the evolution process of natural species, are used to solve optimization problems. Crossover (also called recombination), originated from simulating the chromosome exchange phenomena in zoogamy reproduction, is widely employed in EAs to generate offspring solutions, of which the effectiveness has been examined empirically in applications. However, due to the irregularity of crossover operators and the complicated interactions to mutation, crossover operators are hard to analyze and thus have few theoretical results. Therefore, analyzing crossover not only helps in understanding EAs, but also helps in developing novel techniques for analyzing sophisticated metaheuristic algorithms. In this paper, we derive the General Markov Chain Switching Theorem (GMCST) to facilitate theoretical studies of crossover-enabled EAs. The theorem allows us to analyze the running time of a sophisticated EA from an easy-to-analyze EA. Using this tool, we analyze EAs with several crossover operators on the LeadingOnes and OneMax problems, which are noticeably two well studied problems for mutation-only EAs but with few results for crossover-enabled EAs. We first derive the bounds of running time of the (2+2)-EA with crossover operators; then we study the running time gap between the mutation-only (2:2)-EA and the (2:2)-EA with crossover operators; finally, we develop strategies that apply crossover operators only when necessary, which improve from the mutation-only as well as the crossover-all-the-time (2:2)-EA. The theoretical results are verified by experiments.


Network Characteristics of Video Streaming Traffic

Abstract: Video streaming represents a large fraction of Internet traffic. Surprisingly, little is known about the network characteristics of this traffic. In this paper, we study the network characteristics of the two most popular video streaming services, Netflix and YouTube. We show that the streaming strategies vary with the type of the application (Web browser or native mobile application), and the type of container (Silverlight, Flash, or HTML5) used for video streaming. In particular, we identify three different streaming strategies that produce traffic patterns from non-ack clocked ON-OFF cycles to bulk TCP transfer. We then present an analytical model to study the potential impact of these streaming strategies on the aggregate traffic and make recommendations accordingly.


Computing a Nonnegative Matrix Factorization -- Provably

Abstract: In the Nonnegative Matrix Factorization (NMF) problem we are given an $n \times m$ nonnegative matrix $M$ and an integer $r > 0$. Our goal is to express $M$ as $A W$ where $A$ and $W$ are nonnegative matrices of size $n \times r$ and $r \times m$ respectively. In some applications, it makes sense to ask instead for the product $AW$ to approximate $M$ -- i.e. (approximately) minimize $\norm{M - AW}_F$ where $\norm{}_F$ denotes the Frobenius norm; we refer to this as Approximate NMF. This problem has a rich history spanning quantum mechanics, probability theory, data analysis, polyhedral combinatorics, communication complexity, demography, chemometrics, etc. In the past decade NMF has become enormously popular in machine learning, where $A$ and $W$ are computed using a variety of local search heuristics. Vavasis proved that this problem is NP-complete. We initiate a study of when this problem is solvable in polynomial time: 1. We give a polynomial-time algorithm for exact and approximate NMF for every constant $r$. Indeed NMF is most interesting in applications precisely when $r$ is small. 2. We complement this with a hardness result, that if exact NMF can be solved in time $(nm)^{o(r)}$, 3-SAT has a sub-exponential time algorithm. This rules out substantial improvements to the above algorithm. 3. We give an algorithm that runs in time polynomial in $n$, $m$ and $r$ under the separablity condition identified by Donoho and Stodden in 2003. The algorithm may be practical since it is simple and noise tolerant (under benign assumptions). Separability is believed to hold in many practical settings. To the best of our knowledge, this last result is the first example of a polynomial-time algorithm that provably works under a non-trivial condition on the input and we believe that this will be an interesting and important direction for future work.


Many Sparse Cuts via Higher Eigenvalues

Abstract: Cheeger's fundamental inequality states that any edge-weighted graph has a vertex subset $S$ such that its expansion (a.k.a. conductance) is bounded as follows: \[ \phi(S) \defeq \frac{w(S,\bar{S})}{\min \set{w(S), w(\bar{S})}} \leq 2\sqrt{\lambda_2} \] where $w$ is the total edge weight of a subset or a cut and $\lambda_2$ is the second smallest eigenvalue of the normalized Laplacian of the graph. Here we prove the following natural generalization: for any integer $k \in [n]$, there exist $ck$ disjoint subsets $S_1, ..., S_{ck}$, such that \[ \max_i \phi(S_i) \leq C \sqrt{\lambda_{k} \log k} \] where $\lambda_i$ is the $i^{th}$ smallest eigenvalue of the normalized Laplacian and $c<1,C>0$ are suitable absolute constants. Our proof is via a polynomial-time algorithm to find such subsets, consisting of a spectral projection and a randomized rounding. As a consequence, we get the same upper bound for the small set expansion problem, namely for any $k$, there is a subset $S$ whose weight is at most a $\bigO(1/k)$ fraction of the total weight and $\phi(S) \le C \sqrt{\lambda_k \log k}$. Both results are the best possible up to constant factors. The underlying algorithmic problem, namely finding $k$ subsets such that the maximum expansion is minimized, besides extending sparse cuts to more than one subset, appears to be a natural clustering problem in its own right.


Context-Bounded Analysis For Concurrent Programs With Dynamic Creation of Threads

Abstract: Context-bounded analysis has been shown to be both efficient and effective at finding bugs in concurrent programs. According to its original definition, context-bounded analysis explores all behaviors of a concurrent program up to some fixed number of context switches between threads. This definition is inadequate for programs that create threads dynamically because bounding the number of context switches in a computation also bounds the number of threads involved in the computation. In this paper, we propose a more general definition of context-bounded analysis useful for programs with dynamic thread creation. The idea is to bound the number of context switches for each thread instead of bounding the number of switches of all threads. We consider several variants based on this new definition, and we establish decidability and complexity results for the analysis induced by them.


Sparsity and Robustness in Face Recognition

Abstract: This report concerns the use of techniques for sparse signal representation and sparse error correction for automatic face recognition. Much of the recent interest in these techniques comes from the paper "Robust Face Recognition via Sparse Representation" by Wright et al. (2009), which showed how, under certain technical conditions, one could cast the face recognition problem as one of seeking a sparse representation of a given input face image in terms of a "dictionary" of training images and images of individual pixels. In this report, we have attempted to clarify some frequently encountered questions about this work and particularly, on the validity of using sparse representation techniques for face recognition.


Towards the integration of formal specification in the \'Ancora methodology

Abstract: There are some non-formal methodologies such as RUP, OpenUP, agile methodologies such as SCRUP, XP and techniques like those proposed by UML, which allow the development of software. The software industry has struggled to generate quality software, as importance has not been given to the engineering requirements, resulting in a poor specification of requirements and software of poor quality. In order to generate a contribution to the specification of requirements, this article describes a methodological proposal, implementing formal methods to the results of the process of requirements analysis of the methodology \'Ancora.


Design and Simulation of an 8-bit Dedicated Processor for calculating the Sine and Cosine of an Angle using the CORDIC Algorithm

Abstract: This paper describes the design and simulation of an 8-bit dedicated processor for calculating the Sine and Cosine of an Angle using CORDIC Algorithm (COordinate Rotation DIgital Computer), a simple and efficient algorithm to calculate hyperbolic and trigonometric functions. We have proposed a dedicated processor system, modeled by writing appropriate programs in VHDL, for calculating the Sine and Cosine of an angle. System simulation was carried out using ModelSim 6.3f and Xilinx ISE Design Suite 12.3. A maximum frequency of 81.353 MHz was reached with a minimum period of 12.292 ns. 126 (3%) slices were used. This paper attempts to survey the existing CORDIC algorithm with an eye towards implementation in Field Programmable Gate Arrays (FPGAs). A brief description of the theory behind the algorithm and the derivation of the Sine and Cosine of an angle using the CORDIC algorithm has been presented. The system can be implemented using Spartan3 XC3S400 with Xilinx ISE 12.3 and VHDL.


Securing Biometric Images using Reversible Watermarking

Abstract: Biometric security is a fast growing area. Protecting biometric data is very important since it can be misused by attackers. In order to increase security of biometric data there are different methods in which watermarking is widely accepted. A more acceptable, new important development in this area is reversible watermarking in which the original image can be completely restored and the watermark can be retrieved. But reversible watermarking in biometrics is an understudied area. Reversible watermarking maintains high quality of biometric data. This paper proposes Rotational Replacement of LSB as a reversible watermarking scheme for biometric images. PSNR is the regular method used for quality measurement of biometric data. In this paper we also show that SSIM Index is a better alternate for effective quality assessment for reversible watermarked biometric data by comparing with the well known reversible watermarking scheme using Difference Expansion.


On Three Challenges of Artificial Living Systems and Embodied Evolution

Abstract: Creating autonomous, self-supporting, self-replicating, sustainable systems is a great challenge. To some extent, understanding life means not only being able to create it from scratch, but also improving, supporting, saving it, or even making it even more advanced. This can be thought of as a long-term goal of living technologies and embodied evolution. Current research agenda targets several short- and middle-term steps towards achieving such a vision: connection of ICT and bio-/chemo- developments, advances in "soft" and "wet" robotics, integration of material science into developmental robotics, and potentially, addressing the self-replication in autonomous systems.


Using a Telepresence System to Investigate Route Choice Behavior

Abstract: A combination of a telepresence system and a microscopic traffic simulator is introduced. It is evaluated using a hotel evacuation scenario. Four different kinds of supporting information are compared, standard exit signs, floor plans with indicated exit routes, guiding lines on the floor and simulated agents leading the way. The results indicate that guiding lines are the most efficient way to support an evacuation but the natural behavior of following others comes very close. On another level the results are consistent with previously performed real and virtual experiments and validate the use of a telepresence system in evacuation studies. It is shown that using a microscopic traffic simulator extends the possibilities for evaluation, e.g. by adding simulated humans to the environment.


Tight Bounds on Proper Equivalence Query Learning of DNF

Abstract: We prove a new structural lemma for partial Boolean functions $f$, which we call the seed lemma for DNF. Using the lemma, we give the first subexponential algorithm for proper learning of DNF in Angluin's Equivalence Query (EQ) model. The algorithm has time and query complexity $2^{(\tilde{O}{\sqrt{n}})}$, which is optimal. We also give a new result on certificates for DNF-size, a simple algorithm for properly PAC-learning DNF, and new results on EQ-learning $\log n$-term DNF and decision trees.


Domain decomposition and locality optimization for large-scale lattice Boltzmann simulations

Abstract: We present a simple, parallel and distributed algorithm for setting up and partitioning a sparse representation of a regular discretized simulation domain. This method is scalable for a large number of processes even for complex geometries and ensures load balance between the domains, reasonable communication interfaces, and good data locality within the domain. Applying this scheme to a list-based lattice Boltzmann flow solver can achieve similar or even higher flow solver performance than widely used standard graph partition based tools such as METIS and PT-SCOTCH.


Record-replay debugging for the SCOOP concurrency model

Abstract: To support developers in writing reliable and efficient concurrent programs, novel concurrent programming abstractions have been proposed in recent years. Programming with such abstractions requires new analysis tools because the execution semantics often differs considerably from established models. We present a record-replay technique for programs written in SCOOP, an object-oriented programming model for concurrency. The resulting tool enables developers to reproduce the nondeterministic execution of a concurrent program, a necessary prerequisite for debugging and testing.


Using Transcoding for Hidden Communication in IP Telephony

Abstract: The paper presents a new steganographic method for IP telephony called TranSteg (Transcoding Steganography). Typically, in steganographic communication it is advised for covert data to be compressed in order to limit its size. In TranSteg it is the overt data that is compressed to make space for the steganogram. The main innovation of TranSteg is to, for a chosen voice stream, find a codec that will result in a similar voice quality but smaller voice payload size than the originally selected. Then, the voice stream is transcoded. At this step the original voice payload size is intentionally unaltered and the change of the codec is not indicated. Instead, after placing the transcoded voice payload, the remaining free space is filled with hidden data. TranSteg proof of concept implementation was designed and developed. The obtained experimental results are enclosed in this paper. They prove that the proposed method is feasible and offers a high steganographic bandwidth. TranSteg detection is difficult to perform when performing inspection in a single network localisation.


A Casual Tour Around a Circuit Complexity Bound

Abstract: I will discuss the recent proof that the complexity class NEXP (nondeterministic exponential time) lacks nonuniform ACC circuits of polynomial size. The proof will be described from the perspective of someone trying to discover it.


Hash function based secret sharing scheme designs

Abstract: Secret sharing schemes create an effective method to safeguard a secret by dividing it among several participants. By using hash functions and the herding hashes technique, we first set up a (t+1, n) threshold scheme which is perfect and ideal, and then extend it to schemes for any general access structure. The schemes can be further set up as proactive or verifiable if necessary. The setup and recovery of the secret is efficient due to the fast calculation of the hash function. The proposed scheme is flexible because of the use of existing hash functions.


An architecture for "Web Of Things" using SOCKS protocol based IPv6/IPv4 gatewaying for heterogeneous communication

Abstract: "Web Of Things" evolved from "Internet Of Things". Lot of research has been done in designing architecture for "Web Of Things". Two main architectures are Smart gateway based architecture and embedded Web Server based architecture. These architectures address some of the basic and essential issues relating to Service Oriented Architecture for "Web Of Things". Taking into consideration the period of coexistence of IPv4 and IPv6 we propose an architecture using SOCKS protocol based IPv6/IPv4 gatewaying and refinements which facilitates smooth heterogeneous communications between the IPv6 and IPv4 enabled embedded nodes and can potentially be used to prevent security threats like Denial-of-Service (DoS) attacks on embedded devices attached to the web and increase its performance. Our architecture provides a way for caching responses from device and thereby increasing its efficiency and performance and yielding quick response times.


Covariant fractional extension of the modified Laplace-operator used in 3D-shape recovery

Abstract: Extending the Liouville-Caputo definition of a fractional derivative to a nonlocal covariant generalization of arbitrary bound operators acting on multidimensional Riemannian spaces an appropriate approach for the 3D shape recovery of aperture afflicted 2D slide sequences is proposed. We demonstrate, that the step from a local to a nonlocal algorithm yields an order of magnitude in accuracy and by using the specific fractional approach an additional factor 2 in accuracy of the derived results.


MIVAR: Transition from Productions to Bipartite Graphs MIVAR Nets and Practical Realization of Automated Constructor of Algorithms Handling More than Three Million Production Rules

Abstract: The theoretical transition from the graphs of production systems to the bipartite graphs of the MIVAR nets is shown. Examples of the implementation of the MIVAR nets in the formalisms of matrixes and graphs are given. The linear computational complexity of algorithms for automated building of objects and rules of the MIVAR nets is theoretically proved. On the basis of the MIVAR nets the UDAV software complex is developed, handling more than 1.17 million objects and more than 3.5 million rules on ordinary computers. The results of experiments that confirm a linear computational complexity of the MIVAR method of information processing are given. Keywords: MIVAR, MIVAR net, logical inference, computational complexity, artificial intelligence, intelligent systems, expert systems, General Problem Solver.


Two Classes of Crooked Multinomials Inequivalent to Power Functions

Abstract: It is known that crooked functions can be used to construct many interesting combinatorial objects, and a quadratic function is crooked if and only if it is almost perfect nonlinear (APN). In this paper, we introduce two infinite classes of quadratic crooked multinomials on fields of order $2^{2m}$. One class of APN functions constructed in [7] is a particular case of the one we construct in Theorem 1. Moreover, we prove that the two classes of crooked functions constructed in this paper are EA inequivalent to power functions and conjecture that CCZ inequivalence between them also holds.


An efficient implementation of the simulated annealing heuristic for the quadratic assignment problem

Abstract: The quadratic assignment problem (QAP) is one of the most difficult combinatorial optimization problems. One of the most powerful and commonly used heuristics to obtain approximations to the optimal solution of the QAP is simulated annealing (SA). We present an efficient implementation of the SA heuristic which performs more than 100 times faster then existing implementations for large problem sizes and a large number of SA iterations.


A Compressed Self-Index for Genomic Databases

Abstract: Advances in DNA sequencing technology will soon result in databases of thousands of genomes. Within a species, individuals' genomes are almost exact copies of each other; e.g., any two human genomes are 99.9% the same. Relative Lempel-Ziv (RLZ) compression takes advantage of this property: it stores the first genome uncompressed or as an FM-index, then compresses the other genomes with a variant of LZ77 that copies phrases only from the first genome. RLZ achieves good compression and supports fast random access; in this paper we show how to support fast search as well, thus obtaining an efficient compressed self-index.


Speculative Parallel Evaluation Of Classification Trees On GPGPU Compute Engines

Abstract: We examine the problem of optimizing classification tree evaluation for on-line and real-time applications by using GPUs. Looking at trees with continuous attributes often used in image segmentation, we first put the existing algorithms for serial and data-parallel evaluation on solid footings. We then introduce a speculative parallel algorithm designed for single instruction, multiple data (SIMD) architectures commonly found in GPUs. A theoretical analysis shows how the run times of data and speculative decompositions compare assuming independent processors. To compare the algorithms in the SIMD environment, we implement both on a CUDA 2.0 architecture machine and compare timings to a serial CPU implementation. Various optimizations and their effects are discussed, and results are given for all algorithms. Our specific tests show a speculative algorithm improves run time by 25% compared to a data decomposition.


Confidence Estimation in Structured Prediction

Abstract: Structured classification tasks such as sequence labeling and dependency parsing have seen much interest by the Natural Language Processing and the machine learning communities. Several online learning algorithms were adapted for structured tasks such as Perceptron, Passive- Aggressive and the recently introduced Confidence-Weighted learning . These online algorithms are easy to implement, fast to train and yield state-of-the-art performance. However, unlike probabilistic models like Hidden Markov Model and Conditional random fields, these methods generate models that output merely a prediction with no additional information regarding confidence in the correctness of the output. In this work we fill the gap proposing few alternatives to compute the confidence in the output of non-probabilistic algorithms.We show how to compute confidence estimates in the prediction such that the confidence reflects the probability that the word is labeled correctly. We then show how to use our methods to detect mislabeled words, trade recall for precision and active learning. We evaluate our methods on four noun-phrase chunking and named entity recognition sequence labeling tasks, and on dependency parsing for 14 languages.


Breaking a chaotic image encryption algorithm based on perceptron model

Abstract: Recently, a chaotic image encryption algorithm based on perceptron model was proposed. The present paper analyzes security of the algorithm and finds that the equivalent secret key can be reconstructed with only one pair of known-plaintext/ciphertext, which is supported by both mathematical proof and experiment results. In addition, some other security defects are also reported.


Robust Interactive Learning

Abstract: In this paper we propose and study a generalization of the standard active-learning model where a more general type of query, class conditional query, is allowed. Such queries have been quite useful in applications, but have been lacking theoretical understanding. In this work, we characterize the power of such queries under two well-known noise models. We give nearly tight upper and lower bounds on the number of queries needed to learn both for the general agnostic setting and for the bounded noise model. We further show that our methods can be made adaptive to the (unknown) noise rate, with only negligible loss in query complexity.


Promoting Industry-University Partnership in Information Technology

Abstract: It is becoming increasingly difficult for Nigerian universities to go it alone in terms of serving as a citadel of learning, coping with the huge wage bill and competing with their peers in other parts of the world, due to competitive, economic and other pressures. As a consequence, Nigerian universities are left with no option than to carry their industrial partners along in terms of research and development through the formation of partnerships for their mutual benefit. Since the industries are established for profit making and the universities for knowledge enhancement, such partnerships would help in spreading the costs in terms of provision of knowledge and costs of research. This paper discusses the various types of partnerships involving industries and universities, the benefits derived and a possible model for the working of such a partnership which could be adapted to other sectors and countries in sub-Saharan Africa.


On Bidding with Securities: Risk Aversion and Positive Dependence

Abstract: DeMarzo et al. (2005) consider auctions in which bids are selected from a completely ordered family of securities whose values are tied to the resource being auctioned. The paper defines a notion of relative steepness of families of securities and shows that a steeper family provides greater expected revenue to the seller. Two assumptions are: the buyers are risk-neutral; the random variables through which values and signals of the buyers are realized are affiliated. We show that this revenue ranking holds for the second price auction in the case of risk-aversion. However, it does not hold if affiliation is relaxed to a less restrictive form of positive dependence, namely first order stochastic dominance (FOSD). We define the relative strong steepness of families of securities and show that it provides a necessary and sufficient condition for comparing two families in the FOSD case. All results extend to the English auction.


Multimodal diff-hash

Abstract: Many applications require comparing multimodal data with different structure and dimensionality that cannot be compared directly. Recently, there has been increasing interest in methods for learning and efficiently representing such multimodal similarity. In this paper, we present a simple algorithm for multimodal similarity-preserving hashing, trying to map multimodal data into the Hamming space while preserving the intra- and inter-modal similarities. We show that our method significantly outperforms the state-of-the-art method in the field.


Embedding Description Logic Programs into Default Logic

Abstract: Description logic programs (dl-programs) under the answer set semantics formulated by Eiter {\em et al.} have been considered as a prominent formalism for integrating rules and ontology knowledge bases. A question of interest has been whether dl-programs can be captured in a general formalism of nonmonotonic logic. In this paper, we study the possibility of embedding dl-programs into default logic. We show that dl-programs under the strong and weak answer set semantics can be embedded in default logic by combining two translations, one of which eliminates the constraint operator from nonmonotonic dl-atoms and the other translates a dl-program into a default theory. For dl-programs without nonmonotonic dl-atoms but with the negation-as-failure operator, our embedding is polynomial, faithful, and modular. In addition, our default logic encoding can be extended in a simple way to capture recently proposed weakly well-supported answer set semantics, for arbitrary dl-programs. These results reinforce the argument that default logic can serve as a fruitful foundation for query-based approaches to integrating ontology and rules. With its simple syntax and intuitive semantics, plus available computational results, default logic can be considered an attractive approach to integration of ontology and rules.