Privacy via the Johnson-Lindenstrauss Transform

Abstract: Suppose that party A collects private information about its users, where each user's data is represented as a bit vector. Suppose that party B has a proprietary data mining algorithm that requires estimating the distance between users, such as clustering or nearest neighbors. We ask if it is possible for party A to publish some information about each user so that B can estimate the distance between users without being able to infer any private bit of a user. Our method involves projecting each user's representation into a random, lower-dimensional space via a sparse Johnson-Lindenstrauss transform and then adding Gaussian noise to each entry of the lower-dimensional representation. We show that the method preserves differential privacy---where the more privacy is desired, the larger the variance of the Gaussian noise. Further, we show how to approximate the true distances between users via only the lower-dimensional, perturbed data. Finally, we consider other perturbation methods such as randomized response and draw comparisons to sketch-based methods. While the goal of releasing user-specific data to third parties is more broad than preserving distances, this work shows that distance computations with privacy is an achievable goal.


Stochastic Feature Mapping for PAC-Bayes Classification

Abstract: Probabilistic generative modeling of data distributions can potentially exploit hidden information which is useful for discriminative classification. This observation has motivated the development of approaches that couple generative and discriminative models for classification. In this paper, we propose a new approach to couple generative and discriminative models in an unified framework based on PAC-Bayes risk theory. We first derive the model-parameter-independent stochastic feature mapping from a practical MAP classifier operating on generative models. Then we construct a linear stochastic classifier equipped with the feature mapping, and derive the explicit PAC-Bayes risk bounds for such classifier for both supervised and semi-supervised learning. Minimizing the risk bound, using an EM-like iterative procedure, results in a new posterior over hidden variables (E-step) and the update rules of model parameters (M-step). The derivation of the posterior is always feasible due to the way of equipping feature mapping and the explicit form of bounding risk. The derived posterior allows the tuning of generative models and subsequently the feature mappings for better classification. The derived update rules of the model parameters are same to those of the uncoupled models as the feature mapping is model-parameter-independent. Our experiments show that the coupling between data modeling generative model and the discriminative classifier via a stochastic feature mapping in this framework leads to a general classification tool with state-of-the-art performance.


A Novel Framework using Elliptic Curve Cryptography for Extremely Secure Transmission in Distributed Privacy Preserving Data Mining

Abstract: Privacy Preserving Data Mining is a method which ensures privacy of individual information during mining. Most important task involves retrieving information from multiple data bases which is distributed. The data once in the data warehouse can be used by mining algorithms to retrieve confidential information. The proposed framework has two major tasks, secure transmission and privacy of confidential information during mining. Secure transmission is handled by using elliptic curve cryptography and data distortion for privacy preservation ensuring highly secure environment.


Cloud Computing For Microfinances

Abstract: Evolution of Science and Engineering has led to the growth of several commercial applications. The wide spread implementation of commercial based applications has in turn directed the emergence of advanced technologies such as cloud computing. India has well proven itself as a potential hub for advanced technologies including cloud based industrial market. Microfinance system has emerged out as a panacea to Indian economy since the population encompasses of people who come under poverty and below poverty index. However, one of the key challenges in successful operation of microfinance system in India has given rise to integration of financial services using sophisticated cloud computing model. This paper, therefore propose a fundamental cloud-based microfinance model in order to reduce high transaction risks involved during microfinance operations in an inexpensive and efficient manner.


Genetic Algorithm to Make Persistent Security and Quality of Image in Steganography from RS Analysis

Abstract: Retention of secrecy is one of the significant features during communication activity. Steganography is one of the popular methods to achieve secret communication between sender and receiver by hiding message in any form of cover media such as an audio, video, text, images etc. Least significant bit encoding is the simplest encoding method used by many steganography programs to hide secret message in 24bit, 8bit colour images and grayscale images. Steganalysis is a method of detecting secret message hidden in a cover media using steganography. RS steganalysis is one of the most reliable steganalysis which performs statistical analysis of the pixels to successfully detect the hidden message in an image. However, existing steganography method protects the information against RS steganalysis in grey scale images. This paper presents a steganography method using genetic algorithm to protect against the RS attack in colour images. Stego image is divided into number of blocks. Subsequently, with the implementation of natural evolution on the stego image using genetic algorithm enables to achieve optimized security and image quality.


Application Independent Energy Efficient Data Aggregation in Wireless Sensor Networks

Abstract: Wireless Sensor networks are dense networks of small, low-cost sensors, which collect and disseminate environmental data and thus facilitate monitoring and controlling of physical environment from remote locations with better accuracy. The major challenge is to achieve energy efficiency during the communication among the nodes. This paper aims at proposing a solution to schedule the node's activities to reduce the energy consumption. We propose the construction of a decentralized lifetime maximizing tree within clusters. We aim at minimizing the distance of transmission with minimization of energy consumption. The sensor network is distributed into clusters based on the close proximity of the nodes. Data transfer among the nodes is done with a hybrid technique of both TDMA/ FDMA which leads to efficient utilization of bandwidth and maximizing throughput.


Space-efficient Algorithms for Visibility Problems in Simple Polygon

Abstract: Given a simple polygon $P$ consisting of $n$ vertices, we study the problem of designing space-efficient algorithms for computing (i) the visibility polygon of a point inside $P$, (ii) the weak visibility polygon of a line segment inside $P$ and (iii) the minimum link path between a pair of points inside $P$. For problem (i) two algorithms are proposed. The first one is an in-place algorithm where the input array may be lost. It uses only O(1) extra space apart from the input array. The second one assumes that the input is given in a read-only array, and it needs $O(\sqrt{n})$ extra space. The time complexity of both the algorithms are O(n). For problem (ii), we have assumed that the input polygon is given in a read-only array. Our proposed algorithm runs in $O(n^2)$ time using O(1) extra space. For problem (iii) the time and space complexities of our proposed algorithm are $O(kn)$ and O(1) respectively; $k$ is the length (number of links) in a minimum link path between the given pair of points.


Solution regions in the parameter space of a 3-RRR decoupled robot for a prescribed workspace

Abstract: This paper proposes a new design method to determine the feasible set of parameters of translational or position/orientation decoupled parallel robots for a prescribed singularity-free workspace of regular shape. The suggested method uses Groebner bases to define the singularities and the cylindrical algebraic decomposition to characterize the set of parameters. It makes it possible to generate all the robot designs. A 3-RRR decoupled robot is used to validate the proposed design method.


Lower Bound on Weights of Large Degree Threshold Functions

Abstract: An integer polynomial $p$ of $n$ variables is called a \emph{threshold gate} for a Boolean function $f$ of $n$ variables if for all $x \in \zoon$ $f(x)=1$ if and only if $p(x)\geq 0$. The \emph{weight} of a threshold gate is the sum of its absolute values. In this paper we study how large a weight might be needed if we fix some function and some threshold degree. We prove $2^{\Omega(2^{2n/5})}$ lower bound on this value. The best previous bound was $2^{\Omega(2^{n/8})}$ (Podolskii, 2009). In addition we present substantially simpler proof of the weaker $2^{\Omega(2^{n/4})}$ lower bound. This proof is conceptually similar to other proofs of the bounds on weights of nonlinear threshold gates, but avoids a lot of technical details arising in other proofs. We hope that this proof will help to show the ideas behind the construction used to prove these lower bounds.


A Cooperative Network Coding Strategy for the Interference Relay Channel

Abstract: In this paper, we study an interference relay network with a satellite as relay. We propose a cooperative strategy based on physical layer network coding and superposition modulation decoding for uni-directional communications among users. The performance of our solution in terms of throughput is evaluated through capacity analysis and simulations that include practical constraints such as the lack of synchronization in time and frequency. We demonstrate throughputs significantly larger than the classical time sharing case.


Learning to Rank Query Recommendations by Semantic Similarities

Abstract: Logs of the interactions with a search engine show that users often reformulate their queries. Examining these reformulations shows that recommendations that precise the focus of a query are helpful, like those based on expansions of the original queries. But it also shows that queries that express some topical shift with respect to the original query can help user access more rapidly the information they need. We propose a method to identify from the query logs of past users queries that either focus or shift the initial query topic. This method combines various click-based, topic-based and session based ranking strategies and uses supervised learning in order to maximize the semantic similarities between the query and the recommendations, while at the same diversifying them. We evaluate our method using the query/click logs of a Japanese web search engine and we show that the combination of the three methods proposed is significantly better than any of them taken individually.


Enabling Semantic Analysis of User Browsing Patterns in the Web of Data

Abstract: A useful step towards better interpretation and analysis of the usage patterns is to formalize the semantics of the resources that users are accessing in the Web. We focus on this problem and present an approach for the semantic formalization of usage logs, which lays the basis for eective techniques of querying expressive usage patterns. We also present a query answering approach, which is useful to nd in the logs expressive patterns of usage behavior via formulation of semantic and temporal-based constraints. We have processed over 30 thousand user browsing sessions extracted from usage logs of DBPedia and Semantic Web Dog Food. All these events are formalized semantically using respective domain ontologies and RDF representations of the Web resources being accessed. We show the eectiveness of our approach through experimental results, providing in this way an exploratory analysis of the way users browse theWeb of Data.


Collaboratively Patching Linked Data

Abstract: Today's Web of Data is noisy. Linked Data often needs extensive preprocessing to enable efficient use of heterogeneous resources. While consistent and valid data provides the key to efficient data processing and aggregation we are facing two main challenges: (1st) Identification of erroneous facts and tracking their origins in dynamically connected datasets is a difficult task, and (2nd) efforts in the curation of deficient facts in Linked Data are exchanged rather rarely. Since erroneous data often is duplicated and (re-)distributed by mashup applications it is not only the responsibility of a few original publishers to keep their data tidy, but progresses to be a mission for all distributers and consumers of Linked Data too. We present a new approach to expose and to reuse patches on erroneous data to enhance and to add quality information to the Web of Data. The feasibility of our approach is demonstrated by example of a collaborative game that patches statements in DBpedia data and provides notifications for relevant changes.


Leveraging Usage Data for Linked Data Movie Entity Summarization

Abstract: Novel research in the field of Linked Data focuses on the problem of entity summarization. This field addresses the problem of ranking features according to their importance for the task of identifying a particular entity. Next to a more human friendly presentation, these summarizations can play a central role for semantic search engines and semantic recommender systems. In current approaches, it has been tried to apply entity summarization based on patterns that are inherent to the regarded data. The proposed approach of this paper focuses on the movie domain. It utilizes usage data in order to support measuring the similarity between movie entities. Using this similarity it is possible to determine the k-nearest neighbors of an entity. This leads to the idea that features that entities share with their nearest neighbors can be considered as significant or important for these entities. Additionally, we introduce a downgrading factor (similar to TF-IDF) in order to overcome the high number of commonly occurring features. We exemplify the approach based on a movie-ratings dataset that has been linked to Freebase entities.


The Cost of Perfection for Matchings in Graphs

Abstract: Perfect matchings and maximum weight matchings are two fundamental combinatorial structures. We consider the ratio between the maximum weight of a perfect matching and the maximum weight of a general matching. Motivated by the computer graphics application in triangle meshes, where we seek to convert a triangulation into a quadrangulation by merging pairs of adjacent triangles, we focus mainly on bridgeless cubic graphs. First, we characterize graphs that attain the extreme ratios. Second, we present a lower bound for all bridgeless cubic graphs. Third, we present upper bounds for subclasses of bridgeless cubic graphs, most of which are shown to be tight. Additionally, we present tight bounds for the class of regular bipartite graphs.


How do Ontology Mappings Change in the Life Sciences?

Abstract: Mappings between related ontologies are increasingly used to support data integration and analysis tasks. Changes in the ontologies also require the adaptation of ontology mappings. So far the evolution of ontology mappings has received little attention albeit ontologies change continuously especially in the life sciences. We therefore analyze how mappings between popular life science ontologies evolve for different match algorithms. We also evaluate which semantic ontology changes primarily affect the mappings. We further investigate alternatives to predict or estimate the degree of future mapping changes based on previous ontology and mapping transitions.


Simultaneous Object Detection, Tracking, and Event Recognition

Abstract: The common internal structure and algorithmic organization of object detection, detection-based tracking, and event recognition facilitates a general approach to integrating these three components. This supports multidirectional information flow between these components allowing object detection to influence tracking and event recognition and event recognition to influence tracking and object detection. The performance of the combination can exceed the performance of the components in isolation. This can be done with linear asymptotic complexity.


Video In Sentences Out

Abstract: We present a system that produces sentential descriptions of video: who did what to whom, and where and how they did it. Action class is rendered as a verb, participant objects as noun phrases, properties of those objects as adjectival modifiers in those noun phrases,spatial relations between those participants as prepositional phrases, and characteristics of the event as prepositional-phrase adjuncts and adverbial modifiers. Extracting the information needed to render these linguistic entities requires an approach to event recognition that recovers object tracks, the track-to-role assignments, and changing body posture.


Layered Fixed Point Logic

Abstract: We present a logic for the specification of static analysis problems that goes beyond the logics traditionally used. Its most prominent feature is the direct support for both inductive computations of behaviors as well as co-inductive specifications of properties. Two main theoretical contributions are a Moore Family result and a parametrized worst case time complexity result. We show that the logic and the associated solver can be used for rapid prototyping and illustrate a wide variety of applications within Static Analysis, Constraint Satisfaction Problems and Model Checking. In all cases the complexity result specializes to the worst case time complexity of the classical methods.


Effect of Thread Level Parallelism on the Performance of Optimum Architecture for Embedded Applications

Abstract: According to the increasing complexity of network application and internet traffic, network processor as a subset of embedded processors have to process more computation intensive tasks. By scaling down the feature size and emersion of chip multiprocessors (CMP) that are usually multi-thread processors, the performance requirements are somehow guaranteed. As multithread processors are the heir of uni-thread processors and there isn't any general design flow to design a multithread embedded processor, in this paper we perform a comprehensive design space exploration for an optimum uni-thread embedded processor based on the limited area and power budgets. Finally we run multiple threads on this architecture to find out the maximum thread level parallelism (TLP) based on performance per power and area optimum uni-thread architecture.


An Empirical Study of Spam and Prevention Mechanisms in Online Video Chat Services

Abstract: Recently, online video chat services are becoming increasingly popular. While experiencing tremendous growth, online video chat services have also become yet another spamming target. Unlike spam propagated via traditional medium like emails and social networks, we find that spam propagated via online video chat services is able to draw much larger attention from the users. We have conducted several experiments to investigate spam propagation on Chatroulette - the largest online video chat website. We have found that the largest spam campaign on online video chat websites is dating scams. Our study indicates that spam carrying dating or pharmacy scams have much higher clickthrough rates than email spam carrying the same content. In particular, dating scams reach a clickthrough rate of 14.97%. We also examined and analysed spam prevention mechanisms that online video chat websites have designed and implemented. Our study indicates that the prevention mechanisms either harm legitimate user experience or can be easily bypassed.


An Efficient Cryptographic Hash Algorithm (BSA)

Abstract: Recent cryptanalytic attacks have exposed the vulnerabilities of some widely used cryptographic hash functions like MD5 and SHA-1. Attacks in the line of differential attacks have been used to expose the weaknesses of several other hash functions like RIPEMD, HAVAL. In this paper we propose a new efficient hash algorithm that provides a near random hash output and overcomes some of the earlier weaknesses. Extensive simulations and comparisons with some existing hash functions have been done to prove the effectiveness of the BSA, which is an acronym for the name of the 3 authors.


Seeing Unseeability to See the Unseeable

Abstract: We present a framework that allows an observer to determine occluded portions of a structure by finding the maximum-likelihood estimate of those occluded portions consistent with visible image evidence and a consistency model. Doing this requires determining which portions of the structure are occluded in the first place. Since each process relies on the other, we determine a solution to both problems in tandem. We extend our framework to determine confidence of one's assessment of which portions of an observed structure are occluded, and the estimate of that occluded structure, by determining the sensitivity of one's assessment to potential new observations. We further extend our framework to determine a robotic action whose execution would allow a new observation that would maximally increase one's confidence.


Estimating the Prevalence of Deception in Online Review Communities

Abstract: Consumers' purchase decisions are increasingly influenced by user-generated online reviews. Accordingly, there has been growing concern about the potential for posting "deceptive opinion spam" -- fictitious reviews that have been deliberately written to sound authentic, to deceive the reader. But while this practice has received considerable public attention and concern, relatively little is known about the actual prevalence, or rate, of deception in online review communities, and less still about the factors that influence it. We propose a generative model of deception which, in conjunction with a deception classifier, we use to explore the prevalence of deception in six popular online review communities: Expedia, Hotels.com, Orbitz, Priceline, TripAdvisor, and Yelp. We additionally propose a theoretical model of online reviews based on economic signaling theory, in which consumer reviews diminish the inherent information asymmetry between consumers and producers, by acting as a signal to a product's true, unknown quality. We find that deceptive opinion spam is a growing problem overall, but with different growth rates across communities. These rates, we argue, are driven by the different signaling costs associated with deception for each review community, e.g., posting requirements. When measures are taken to increase signaling cost, e.g., filtering reviews written by first-time reviewers, deception prevalence is effectively reduced.


Performance-Optimum Superscalar Architecture for Embedded Applications

Abstract: Embedded applications are widely used in portable devices such as wireless phones, personal digital assistants, laptops, etc. High throughput and real time requirements are especially important in such data-intensive tasks. Therefore, architectures that provide the required performance are the most desirable. On the other hand, processor performance is severely related to the average memory access delay, number of processor registers and also size of the instruction window and superscalar parameters. Therefore, cache, register file and superscalar parameters are the major architectural concerns in designing a superscalar architecture for embedded processors. Although increasing cache and register file size leads to performance improvements in high performance embedded processors, the increased area, power consumption and memory delay are the overheads of these techniques. This paper explores the effect of cache, register file and superscalar parameters on the processor performance to specify the optimum size of these parameters for embedded applications. Experimental results show that although having bigger size of these parameters is one of the performance improvement approaches in embedded processors, however, by increasing the size of some parameters over a threshold value, performance improvement is saturated and especially in cache size, increments over this threshold value decrease the performance.


Watersheds, waterfalls, on edge or node weighted graphs

Abstract: We present an algebraic approach to the watershed adapted to edge or node weighted graphs. Starting with the flooding adjunction, we introduce the flooding graphs, for which node and edge weights may be deduced one from the other. Each node weighted or edge weighted graph may be transformed in a flooding graph, showing that there is no superiority in using one or the other, both being equivalent. We then introduce pruning operators extract subgraphs of increasing steepness. For an increasing steepness, the number of never ascending paths becomes smaller and smaller. This reduces the watershed zone, where catchment basins overlap. A last pruning operator called scissor associates to each node outside the regional minima one and only one edge. The catchment basins of this new graph do not overlap and form a watershed partition. Again, with an increasing steepness, the number of distinct watershed partitions contained in a graph becomes smaller and smaller. Ultimately, for natural image, an infinite steepness leads to a unique solution, as it is not likely that two absolutely identical non ascending paths of infinite steepness connect a node with two distinct minima. It happens that non ascending paths of a given steepness are the geodesics of lexicographic distance functions of a given depth. This permits to extract the watershed partitions as skeletons by zone of influence of the minima for such lexicographic distances. The waterfall hierarchy is obtained by a sequence of operations. The first constructs the minimum spanning forest which spans an initial watershed partition. The contraction of the trees into one node produces a reduced graph which may be submitted to the same treatment. The process is iterated until only one region remains. The union of the edges of all forests produced constitutes a minimum spanning tree of the initial graph.


On Vertex Sparsifiers with Steiner Nodes

Abstract: Given an undirected graph $G=(V,E)$ with edge capacities $c_e\geq 1$ for $e\in E$ and a subset $T$ of $k$ vertices called terminals, we say that a graph $H$ is a quality-$q$ cut sparsifier for $G$ iff $T\subseteq V(H)$, and for any partition $(A,B)$ of $T$, the values of the minimum cuts separating $A$ and $B$ in graphs $G$ and $H$ are within a factor $q$ from each other. We say that $H$ is a quality-$q$ flow sparsifier for $G$ iff $T\subseteq V(H)$, and for any set $D$ of demands over the terminals, the values of the minimum edge congestion incurred by fractionally routing the demands in $D$ in graphs $G$ and $H$ are within a factor $q$ from each other. So far vertex sparsifiers have been studied in a restricted setting where the sparsifier $H$ is not allowed to contain any non-terminal vertices, that is $V(H)=T$. For this setting, efficient algorithms are known for constructing quality-$O(\log k/\log\log k)$ cut and flow vertex sparsifiers, as well as a lower bound of $\tilde{\Omega}(\sqrt{\log k})$ on the quality of any flow or cut sparsifier. We study flow and cut sparsifiers in the more general setting where Steiner vertices are allowed, that is, we no longer require that $V(H)=T$. We show algorithms to construct constant-quality cut sparsifiers of size $O(C^3)$ in time $\poly(n)\cdot 2^C$, and constant-quality flow sparsifiers of size $C^{O(\log\log C)}$ in time $n^{O(\log C)}\cdot 2^C$, where $C$ is the total capacity of the edges incident on the terminals.


Segmentation Similarity and Agreement

Abstract: We propose a new segmentation evaluation metric, called segmentation similarity (S), that quantifies the similarity between two segmentations as the proportion of boundaries that are not transformed when comparing them using edit distance, essentially using edit distance as a penalty function and scaling penalties by segmentation size. We propose several adapted inter-annotator agreement coefficients which use S that are suitable for segmentation. We show that S is configurable enough to suit a wide variety of segmentation evaluations, and is an improvement upon the state of the art. We also propose using inter-annotator agreement coefficients to evaluate automatic segmenters in terms of human performance.


Secure Comparison Without Explicit XOR

Abstract: We propose an efficient protocol for secure comparison of integers when both integers are shared between two parties. Such protocols are useful for implementing secure auctions. The proposed protocol's computational complexity is roughly half the complexity of the best known efficient protocol. The efficiency of the proposed protocol stems from the removal of the XOR computation which is a time consuming operation.


Synthesis of Minimal Error Control Software

Abstract: Software implementations of controllers for physical systems are at the core of many embedded systems. The design of controllers uses the theory of dynamical systems to construct a mathematical control law that ensures that the controlled system has certain properties, such as asymptotic convergence to an equilibrium point, while optimizing some performance criteria. However, owing to quantization errors arising from the use of fixed-point arithmetic, the implementation of this control law can only guarantee practical stability: under the actions of the implementation, the trajectories of the controlled system converge to a bounded set around the equilibrium point, and the size of the bounded set is proportional to the error in the implementation. The problem of verifying whether a controller implementation achieves practical stability for a given bounded set has been studied before. In this paper, we change the emphasis from verification to automatic synthesis. Using synthesis, the need for formal verification can be considerably reduced thereby reducing the design time as well as design cost of embedded control software. We give a methodology and a tool to synthesize embedded control software that is Pareto optimal w.r.t. both performance criteria and practical stability regions. Our technique is a combination of static analysis to estimate quantization errors for specific controller implementations and stochastic local search over the space of possible controllers using particle swarm optimization. The effectiveness of our technique is illustrated using examples of various standard control systems: in most examples, we achieve controllers with close LQR-LQG performance but with implementation errors, hence regions of practical stability, several times as small.


Adaptive Framework for Data Distribution in Wireless Sensor Networks

Abstract: In recent years, the wireless sensor network (WSN) is playing a key role in sensing, collecting and disseminating information in various applications. An important feature associated with WSN is to develop an efficient data distribution and routing scheme to ensure better quality of service (QoS) that reduces the power consumption and the end-to-end data delivery time. In this work, we propose an adaptive framework to transmit data packets from a source to the sink in WSN across multiples paths with strategically distributed data packets so as to minimize the power consumption as well as the end-to-end data delivery time.


Multisource Adaptive Data Distribution and Routing in Wireless Sensor Networks

Abstract: The wireless sensor network is a collection of energy-constrained nodes. Their objective is to sense, collect and process information for some ad-hoc purpose. Typically the nodes are deployed in geographically inaccessible regions. Thus the most challenging task is to design a network with minimal power consumption. As the nodes have to collect and process data very fast, minimizing data delivery time is another objective. In addition to this, when multiple sources transmit data simultaneously, the network load gradually increases and it may lead to congestion. In this paper we have proposed an adaptive framework in which multiple sources transmit data simultaneously with minimal end-to-end data delivery time and minimal energy consumption besides ensuring that congestion remains at an optimum low so that minimal number of data packets are dropped. This paper presents an adaptive framework to achieve the above-mentioned objectives. This framework has been used over Mac 802.11 and extensive simulations have been carried out in NS2 to prove the effectiveness of the framework over traditional Mac as well as few other existing protocols.


Designing an Energy Efficient Framework for Data Gathering in Wireless Sensor Network

Abstract: Wireless sensor network (WSN) is a collection of nodes which can communicate with each other without any prior infrastructure along with the ability to collect data autonomously and effectively after being deployed in an ad-hoc fashion to monitor a given area. One major problem encountered in data gathering wireless systems is to obtain an optimal balance among the number of nodes deployed, energy efficiency and lifetime as energy of nodes cannot be replenished. In this paper we propose first a scheme to estimate the number of nodes to be deployed in a WSN for a predetermined lifetime so that total energy utilization and complete connectivity are ensured under all circumstances. This scheme also guarantees that during each data gathering cycle, every node dissipates the requisite amount of energy, which thus minimizes the number of nodes required to achieve the desired network lifetime. Second, this paper has proposed a framework to conduct data gathering in WSN. Extensive simulations have been carried out in ns2 to establish the effectiveness of this framework.


Disconnectivity and Relative Positions in Simultaneous Embeddings

Abstract: The problem Simultaneous Embedding with Fixed Edges (SEFE) asks for two planar graph $G^1 = (V^1, E^1)$ and $G^2 = (V^2, E^2)$ sharing a common subgraph $G = G^1 \cap G^2$ whether they admit planar drawings such that the common graph is drawn the same in both. Previous results on this problem require $G$, $G^1$ and $G^2$ to be connected. This paper is a first step towards solving instances where these graphs are disconnected. First, we show that an instance of the general SEFE-problem can be reduced in linear time to an equivalent instance where $V^1 = V^2$ and $G^1$ and $G^2$ are connected. This shows that it can be assumed without loss of generality that both input graphs are connected. Second, we consider instances where $G$ is disconnected. We show that SEFE can be solved in linear time if $G$ is a family of disjoint cycles by introducing the CC-tree, which represents all simultaneous embeddings. We extend these results (including the CC-tree) to the case where $G$ consists of arbitrary connected components, each with a fixed embedding. Note that previous results require $G$ to be connected and thus do not need to care about relative positions of connected components. By contrast, we assume the embedding of each connected component to be fixed and thus focus on these relative positions. As SEFE requires to deal with both, embeddings of connected components and their relative positions, this complements previous work.


Non-sparse Linear Representations for Visual Tracking with Online Reservoir Metric Learning

Abstract: Most sparse linear representation-based trackers need to solve a computationally expensive L1-regularized optimization problem. To address this problem, we propose a visual tracker based on non-sparse linear representations, which admit an efficient closed-form solution without sacrificing accuracy. Moreover, in order to capture the correlation information between different feature dimensions, we learn a Mahalanobis distance metric in an online fashion and incorporate the learned metric into the optimization problem for obtaining the linear representation. We show that online metric learning using proximity comparison significantly improves the robustness of the tracking, especially on those sequences exhibiting drastic appearance changes. Furthermore, in order to prevent the unbounded growth in the number of training samples for the metric learning, we design a time-weighted reservoir sampling method to maintain and update limited-sized foreground and background sample buffers for balancing sample diversity and adaptability. Experimental results on challenging videos demonstrate the effectiveness and robustness of the proposed tracker.


A Kuratowski-Type Theorem for Planarity of Partially Embedded Graphs

Abstract: A partially embedded graph (or PEG) is a triple (G,H,\H), where G is a graph, H is a subgraph of G, and \H is a planar embedding of H. We say that a PEG (G,H,\H) is planar if the graph G has a planar embedding that extends the embedding \H. We introduce a containment relation of PEGs analogous to graph minor containment, and characterize the minimal non-planar PEGs with respect to this relation. We show that all the minimal non-planar PEGs except for finitely many belong to a single easily recognizable and explicitly described infinite family. We also describe a more complicated containment relation which only has a finite number of minimal non-planar PEGs. Furthermore, by extending an existing planarity test for PEGs, we obtain a polynomial-time algorithm which, for a given PEG, either produces a planar embedding or identifies an obstruction.


Proving Termination of Probabilistic Programs Using Patterns

Abstract: Proving programs terminating is a fundamental computer science challenge. Recent research has produced powerful tools that can check a wide range of programs for termination. The analog for probabilistic programs, namely termination with probability one ("almost-sure termination"), is an equally important property for randomized algorithms and probabilistic protocols. We suggest a novel algorithm for proving almost-sure termination of probabilistic programs. Our algorithm exploits the power of state-of-the-art model checkers and termination provers for nonprobabilistic programs: it calls such tools within a refinement loop and thereby iteratively constructs a "terminating pattern", which is a set of terminating runs with probability one. We report on various case studies illustrating the effectiveness of our algorithm. As a further application, our algorithm can improve lower bounds on reachability probabilities.


Improved Randomized Online Scheduling of Intervals and Jobs

Abstract: We study the online preemptive scheduling of intervals and jobs (with restarts). Each interval or job has an arrival time, a deadline, a length and a weight. The objective is to maximize the total weight of completed intervals or jobs. While the deterministic case for intervals was settled a long time ago, the randomized case remains open. In this paper we first give a 2-competitive randomized algorithm for the case of equal length intervals. The algorithm is barely random in the sense that it randomly chooses between two deterministic algorithms at the beginning and then sticks with it thereafter. Then we extend the algorithm to cover several other cases of interval scheduling including monotone instances, C-benevolent instances and D-benevolent instances, giving the same competitive ratio. These algorithms are surprisingly simple but have the best competitive ratio against all previous (fully or barely) randomized algorithms. Next we extend the idea to give a 3-competitive algorithm for equal length jobs. Finally, we prove a lower bound of 2 on the competitive ratio of all barely random algorithms that choose between two deterministic algorithms for scheduling equal length intervals (and hence jobs).


An Equilibrium Analysis of Scrip Systems

Abstract: A game-theoretic model of scrip (artificial currency) systems is analyzed. It is shown that relative entropy can be used to characterize the distribution of agent wealth when all agents use threshold strategies---that is, they volunteer to do work iff they have below a threshold amount of money. Monotonicity of agents' best-reply functions is used to show that scrip systems have pure strategy equilibria where all agents use threshold strategies. An algorithm is given that can compute such an equilibrium and the resulting distribution of wealth.


Label-Guided Graph Exploration with Adjustable Ratio of Labels

Abstract: The graph exploration problem is to visit all the nodes of a connected graph by a mobile entity, e.g., a robot. The robot has no a priori knowledge of the topology of the graph or of its size. Cohen et al. \cite{Ilcinkas08} introduced label guided graph exploration which allows the system designer to add short labels to the graph nodes in a preprocessing stage; these labels can guide the robot in the exploration of the graph. In this paper, we address the problem of adjustable 1-bit label guided graph exploration. We focus on the labeling schemes that not only enable a robot to explore the graph but also allow the system designer to adjust the ratio of the number of different labels. This flexibility is necessary when maintaining different labels may have different costs or when the ratio is pre-specified. We present 1-bit labeling (two colors, namely black and white) schemes for this problem along with a labeling algorithm for generating the required labels. Given an $n$-node graph and a rational number $\rho$, we can design a 1-bit labeling scheme such that $n/b\geq \rho$ where $b$ is the number of nodes labeled black. The robot uses $O(\rho\log\Delta)$ bits of memory for exploring all graphs of maximum degree $\Delta$. The exploration is completed in time $O(n\Delta^{\frac{16\rho+7}{3}}/\rho+\Delta^{\frac{40\rho+10}{3}})$. Moreover, our labeling scheme can work on graphs containing loops and multiple edges, while that of Cohen et al. focuses on simple graphs.


Tackling the testing migration problem with SAT-Solvers

Abstract: We show that it is feasible to formulate the testing migration problem as a practically solvable PMAX-SAT instance, when package dependencies and conflicts are pre-processed sensibly.


Parameterized Resolution with bounded conjunction

Abstract: We provide separations between the parameterized versions of Res(1) (Resolution) and Res(2). Using a different set of parameterized contradictions, we also separate the parameterized versions of Res*(1) (tree-Resolution) and Res*(2).


STP/HAMPI and Computer Security

Abstract: In the past several years I have written two SMT solvers called STP and HAMPI that have found widespread use in computer security research by leading groups in academia, industry and the government. In this brief note I summarize the features of STP/HAMPI that make them particularly suited for computer security research, and a listing of some of the more important projects that use them.


Reasoning on Schemata of Formulae

Abstract: A logic is presented for reasoning on iterated sequences of formulae over some given base language. The considered sequences, or "schemata", are defined inductively, on some algebraic structure (for instance the natural numbers, the lists, the trees etc.). A proof procedure is proposed to relate the satisfiability problem for schemata to that of finite disjunctions of base formulae. It is shown that this procedure is sound, complete and terminating, hence the basic computational properties of the base language can be carried over to schemata.


Collective Intelligence 2012: Proceedings

Abstract: This volume holds the proceedings of the Collective Intelligence 2012 conference in Cambridge, Massachusetts. It contains the full papers, poster papers, and plenary abstracts. Collective intelligence has existed at least as long as humans have, because families, armies, countries, and companies have all - at least sometimes - acted collectively in ways that seem intelligent. But in the last decade or so a new kind of collective intelligence has emerged: groups of people and computers, connected by the Internet, collectively doing intelligent things. For example, Google technology harvests knowledge generated by millions of people creating and linking web pages and then uses this knowledge to answer queries in ways that often seem amazingly intelligent. Or in Wikipedia, thousands of people around the world have collectively created a very large and high quality intellectual product with almost no centralized control, and almost all as volunteers! These early examples of Internet-enabled collective intelligence are not the end of the story but just the beginning. And in order to understand the possibilities and constraints of these new kinds of intelligence, we need a new interdisciplinary field.


TeXmacs-Reduce interface

Abstract: This tutorial (based on the talk at the TeXmacs workshop in Faro, Portugal, February 26 - March 2, 2012) describes the new and improved Reduce plugin in GNU TeXmacs.


Definability of linear equation systems over groups and rings

Abstract: Motivated by the quest for a logic for PTIME and recent insights that the descriptive complexity of problems from linear algebra is a crucial aspect of this problem, we study the solvability of linear equation systems over finite groups and rings from the viewpoint of logical (inter-)definability. All problems that we consider are decidable in polynomial time, but not expressible in fixed-point logic with counting. They also provide natural candidates for a separation of polynomial time from rank logics, which extend fixed-point logics by operators for determining the rank of definable matrices and which are sufficient for solvability problems over fields. Based on the structure theory of finite rings, we establish logical reductions among various solvability problems. Our results indicate that all solvability problems for linear equation systems that separate fixed-point logic with counting from PTIME can be reduced to solvability over commutative rings. Moreover, we prove closure properties for classes of queries that reduce to solvability over rings, which provides normal forms for logics extended with solvability operators. We conclude by studying the extent to which fixed-point logic with counting can express problems in linear algebra over finite commutative rings, generalising known results on the logical definability of linear-algebraic problems over finite fields.


Tractable Answer-Set Programming with Weight Constraints: Bounded Treewidth is not Enough

Abstract: Cardinality constraints or, more generally, weight constraints are well recognized as an important extension of answer-set programming. Clearly, all common algorithmic tasks related to programs with cardinality or weight constraints - like checking the consistency of a program - are intractable. Many intractable problems in the area of knowledge representation and reasoning have been shown to become linear time tractable if the treewidth of the programs or formulas under consideration is bounded by some constant. The goal of this paper is to apply the notion of treewidth to programs with cardinality or weight constraints and to identify tractable fragments. It will turn out that the straightforward application of treewidth to such class of programs does not suffice to obtain tractability. However, by imposing further restrictions, tractability can be achieved.


Heterogeneous Highly Parallel Implementation of Matrix Exponentiation Using GPU

Abstract: The vision of super computer at every desk can be realized by powerful and highly parallel CPUs or GPUs or APUs. Graphics processors once specialized for the graphics applications only, are now used for the highly computational intensive general purpose applications. Very expensive GFLOPs and TFLOP performance has become very cheap with the GPGPUs. Current work focuses mainly on the highly parallel implementation of Matrix Exponentiation. Matrix Exponentiation is widely used in many areas of scientific community ranging from highly critical flight, CAD simulations to financial, statistical applications. Proposed solution for Matrix Exponentiation uses OpenCL for exploiting the hyper parallelism offered by the many core GPGPUs. It employs many general GPU optimizations and architectural specific optimizations. This experimentation covers the optimizations targeted specific to the Scientific Graphics cards (Tesla-C2050). Heterogeneous Highly Parallel Matrix Exponentiation method has been tested for matrices of different sizes and with different powers. The devised Kernel has shown 1000X speedup and 44 fold speedup with the naive GPU Kernel.


Building Fastest Broadcast Trees in Periodically-Varying Graphs

Abstract: Delay-tolerant networks (DTNs) are characterized by a possible absence of end-to-end communication routes at any instant. Still, connectivity can generally be established over time and space. The optimality of a temporal path (journey) in this context can be defined in several terms, including topological (e.g. {\em shortest} in hops) and temporal (e.g. {\em fastest, foremost}). The combinatorial problem of computing shortest, foremost, and fastest journeys {\em given full knowledge} of the network schedule was addressed a decade ago (Bui-Xuan {\it et al.}, 2003). A recent line of research has focused on the distributed version of this problem, where foremost, shortest or fastest {\em broadcast} are performed without knowing the schedule beforehand. In this paper we show how to build {\em fastest} broadcast trees (i.e., trees that minimize the global duration of the broadcast, however late the departure is) in Time-Varying Graphs where intermittent edges are available periodically (it is known that the problem is infeasible in the general case even if various parameters of the graph are know). We address the general case where contacts between nodes can have arbitrary durations and thus fastest routes may consist of a mixture of {\em continuous} and {\em discontinuous} segments (a more complex scenario than when contacts are {\em punctual} and thus routes are only discontinuous). Using the abstraction of \tclocks to compute the temporal distances, we solve the fastest broadcast problem by first learning, at the emitter, what is its time of {\em minimum temporal eccentricity} (i.e. the fastest time to reach all the other nodes), and second by building a {\em foremost} broadcast tree relative to this particular emission date.


Alternative Tilings for the Fast Multipole Method on the Plane

Abstract: The fast multipole method (FMM) performs fast approximate kernel summation to a specified tolerance $\epsilon$ by using a hierarchical division of the domain, which groups source and receiver points into regions that satisfy local separation and the well-separated pair decomposition properties. While square tilings and quadtrees are commonly used in 2D, we investigate alternative tilings and associated spatial data structures: regular hexagons (septree) and triangles (triangle-quadtree). We show that both structures satisfy separation properties for the FMM and prove their theoretical error bounds and computational costs. Empirical runtime and error analysis of our implementations are provided.


Algorithms for Junctions in Directed Acyclic Graphs

Abstract: Given a pair of distinct vertices u, v in a graph G, we say that s is a junction of u, v if there are in G internally vertex disjoint directed paths from s to u and from s to v. We show how to characterize junctions in directed acyclic graphs. We also consider the two problems in the following and derive efficient algorithms to solve them. Given a directed acyclic graph G and a vertex s in G, how can we find all pairs of vertices of G such that s is a junction of them? And given a directed acyclic graph G and k pairs of vertices of G, how can we preprocess G such that all junctions of k given pairs of vertices could be listed quickly? All junctions of k pairs problem arises in an application in Anthropology and we apply our algorithm to find such junctions on kinship networks of some brazilian indian ethnic groups.


Secure Tracking in Sensor Networks using Adaptive Extended Kalman Filter

Abstract: Location information of sensor nodes has become an essential part of many applications in Wireless Sensor Networks (WSN). The importance of location estimation and object tracking has made them the target of many security attacks. Various methods have tried to provide location information with high accuracy, while lots of them have neglected the fact that WSNs may be deployed in hostile environments. In this paper, we address the problem of securely tracking a Mobile Node (MN) which has been noticed very little previously. A novel secure tracking algorithm is proposed based on Extended Kalman Filter (EKF) that is capable of tracking a Mobile Node (MN) with high resolution in the presence of compromised or colluding malicious beacon nodes. It filters out and identifies the malicious beacon data in the process of tracking. The proposed method considerably outperforms the previously proposed secure algorithms in terms of either detection rate or MSE. The experimental data based on different settings for the network has shown promising results.


A recursive normalizing one-step reduction strategy for the distributive lambda calculus

Abstract: We positively answer the question A.1.6 in J. Klop's "Ustica Notes": "Is there a recursive normalizing one-step reduction strategy for micro $\lambda$-calculus?" Micro $\lambda$-calculus refers to an implementation of the $\lambda$-calculus due to Revesz, implementing $\beta$-reduction by means of "micro steps" recursively distributing a $\beta$-redex $(\lambda x.M)\ N$ over its body $M$.


Teaching Chemistry in a Social Learning Environment: Facing Drivers and Barriers

Abstract: The Portuguese Technological Plan for Education (TPE) was established to modernize schools and to consolidate the role of Information and Communication Technologies (ICT) in order to promote the academic success of students and allow schools to be transformed into technological enriched environments through a significant learning and knowledge building in a participatory, collaborative and sharing logic. With this work we aimed to establish dynamical interactions students-content- teacher in order to overcome a diagnosed students' lack of effort towards studying curriculum chemistry content. Our methodology design is a theoretical and descriptive one, carried out in a secondary school during the 2009/2010 school year, in order to answer the question "How to improve the engagement of K-12 students in chemistry classes?". Students, gathered in small groups were asked to create digital learning resources (DLR) during classes. The teacher assumed the role of the supervisor, coacher and facilitator of every task that had to be taken or chosen by the students. To enhance interaction student-student and student-teacher, a Twitter account and a Ning site were created for the class. Both supported the Social Learning Environment (SLE) that was intended to be created. The data collected led us to satisfactory results in what concerns the goals of the study. The affordances and constraints of SLE as an open architecture that has potential to facilitate collaborative learning are delineated. Future work should focus on mechanisms that allow assessment both of the methodology used and the students' generated content in order to improve students' learning in this environment.


Analyzing Nonblocking Switching Networks using Linear Programming (Duality)

Abstract: The main task in analyzing a switching network design (including circuit-, multirate-, and photonic-switching) is to determine the minimum number of some switching components so that the design is non-blocking in some sense (e.g., strict- or wide-sense). We show that, in many cases, this task can be accomplished with a simple two-step strategy: (1) formulate a linear program whose optimum value is a bound for the minimum number we are seeking, and (2) specify a solution to the dual program, whose objective value by weak duality immediately yields a sufficient condition for the design to be non-blocking. We illustrate this technique through a variety of examples, ranging from circuit to multirate to photonic switching, from unicast to $f$-cast and multicast, and from strict- to wide-sense non-blocking. The switching architectures in the examples are of Clos-type and Banyan-type, which are the two most popular architectural choices for designing non-blocking switching networks. To prove the result in the multirate Clos network case, we formulate a new problem called {\sc dynamic weighted edge coloring} which generalizes the {\sc dynamic bin packing} problem. We then design an algorithm with competitive ratio 5.6355 for the problem. The algorithm is analyzed using the linear programming technique. A new upper-bound for multirate wide-sense non-blocking Clos networks follow, improving upon a decade-old bound on the same problem.


Intelligent Database Flexible Querying System by Approximate Query Processing

Abstract: Database flexible querying is an alternative to the classic one for users. The use of Formal Concepts Analysis (FCA) makes it possible to make approximate answers that those turned over by a classic DataBase Management System (DBMS). Some applications do not need exact answers. However, flexible querying can be expensive in response time. This time is more significant when the flexible querying require the calculation of aggregate functions ("Sum", "Avg", "Count", "Var" etc.). In this paper, we propose an approach which tries to solve this problem by using Approximate Query Processing (AQP).


Towards Fuzzy-Hard Clustering Mapping Processes

Abstract: Although the validation step can appear crucial in the case of clustering adopting fuzzy approaches, the problem of the partition validity obtained by those adopting the hard ones was not tackled. To cure this problem, we propose in this paper fuzzy-hard mapping processes of clustering while benefitting from those adopting the fuzzy case. These mapping processes concern: (1) local and global clustering evaluation measures: the first for the detection of the "worst" clusters to merging or splitting them. The second relates to the evaluation of the obtained partition for each iteration, (2) merging and splitting processes taking into account the proposed measures, and (3) automatic clustering algorithms implementing these new concepts.


Information, Community, and Action: How Nonprofit Organizations Use Social Media

Abstract: The rapid diffusion of "microblogging" services such as Twitter is ushering in a new era of possibilities for organizations to communicate with and engage their core stakeholders and the general public. To enhance understanding of the communicative functions microblogging serves for organizations, this study examines the Twitter utilization practices of the 100 largest nonprofit organizations in the United States. The analysis reveals there are three key functions of microblogging updates-"information," "community," and "action." Though the informational use of microblogging is extensive, nonprofit organizations are better at using Twitter to strategically engage their stakeholders via dialogic and community-building practices than they have been with traditional websites. The adoption of social media appears to have engendered new paradigms of public engagement. Keywords: microblogging; Twitter; social media; stakeholder relations; organizational communication; organization-public relations; nonprofit organizations


Mixing Times of Self-Organizing Lists and Biased Permutations

Abstract: Sampling permutations from S_n is a fundamental problem from probability theory. The nearest neighbor transposition chain \cal{M}}_{nn} is known to converge in time \Theta(n^3 \log n) in the uniform case and time \Theta(n^2) in the constant bias case, in which we put adjacent elements in order with probability p \neq 1/2 and out of order with probability 1-p. Here we consider the variable bias case where we put adjacent elements x<y in order with probability p{x,y} and out of order with probability 1-p_{x,y}. The problem of bounding the mixing rate of M_{nn} was posed by Fill and was motivated by the Move-Ahead-One self-organizing list update algorithm. It was conjectured that the chain would always be rapidly mixing if 1/2 \leq p_{x,y} \leq 1 for all x < y, but this was only known in the case of constant bias or when p_{x,y} is equal to 1/2 or 1, a case that corresponds to sampling linear extensions of a partial order. We prove the chain is rapidly mixing for two classes: "Choose Your Weapon," where we are given r_1,..., r_{n-1} with r_i \geq 1/2 and p_{x,y}=r_x for all x<y (so the dominant player chooses the game, thus fixing his or her probability of winning), and "League Hierarchies," where there are two leagues and players from the A-league have a fixed probability of beating players from the B-league, players within each league are similarly divided into sub-leagues with a possibly different fixed probability, and so forth recursively. Both of these classes include permutations with constant bias as a special case. Moreover, we also prove that the most general conjecture is false by constructing a counterexample where 1/2 \leq p_{x,y} \leq 1 for all x< y, but for which the nearest neighbor transposition chain requires exponential time to converge.


An Efficient Finite Tree Automata Library

Abstract: Numerous computer systems use dynamic control and data structures of unbounded size. These data structures have often the character of trees or they can be encoded as trees with some additional pointers. This is exploited by some currently intensively studied techniques of formal verification that represent an infinite number of states using a finite tree automaton. However, currently there is no tree automata library implementation that would provide an efficient and flexible support for such methods. Thus the aim of this Master's Thesis is to provide such a library. The present paper first describes the theoretical background of finite tree automata and regular tree languages. Then it surveys the current implementations of tree automata libraries and studies various verification techniques, outlining requirements for the library. Representation of a finite tree automaton and algorithms that perform standard language operations on this representation are proposed in the next part, which is followed by description of library implementation. Through a series of experiments it is shown that the library can compete with other available tree automata libraries, in certain areas being even significantly superior to them.


Monografia

Abstract: Developed structural scheme implementation of an integrated security and formulated principles for the creation and development of an effective system of information security.


Process algebra with conditionals in the presence of epsilon

Abstract: In a previous paper, we presented several extensions of ACP with conditional expressions, including one with a retrospection operator on conditions to allow for looking back on conditions under which preceding actions have been performed. In this paper, we add a constant for a process that is only capable of terminating successfully to those extensions of ACP, which can be very useful in applications. It happens that in all cases the addition of this constant is unproblematic.


Lower Complexity Bounds for Lifted Inference

Abstract: One of the big challenges in the development of probabilistic relational (or probabilistic logical) modeling and learning frameworks is the design of inference techniques that operate on the level of the abstract model representation language, rather than on the level of ground, propositional instances of the model. Numerous approaches for such "lifted inference" techniques have been proposed. While it has been demonstrated that these techniques will lead to significantly more efficient inference on some specific models, there are only very recent and still quite restricted results that show the feasibility of lifted inference on certain syntactically defined classes of models. Lower complexity bounds that imply some limitations for the feasibility of lifted inference on more expressive model classes were established early on in (Jaeger 2000). However, it is not immediate that these results also apply to the type of modeling languages that currently receive the most attention, i.e., weighted, quantifier-free formulas. In this paper we extend these earlier results, and show that under the assumption that NETIME =/= ETIME, there is no polynomial lifted inference algorithm for knowledge bases of weighted, quantifier- and function-free formulas. Further strengthening earlier results, this is also shown to hold for approximate inference, and for knowledge bases not containing the equality predicate.


Assessing and improving an approach to delay-tolerant networking

Abstract: Delay-tolerant networking (DTN) is a term invented to describe and encompass all types of long-delay, disconnected, disrupted or intermittently-connected networks, where mobility and outages or scheduled contacts may be experienced. 'DTN' is also used to refer to the Bundle Protocol, which has been proposed as the one unifying solution for disparate DTN networking scenarios, after originally being designed solely for use in deep space for the 'Interplanetary Internet.' We evaluated the Bundle Protocol by testing it in space and on the ground. We have found architectural weaknesses in the Bundle Protocol that may prevent engineering deployment of this protocol in realistic delay-tolerant networking scenarios, and have proposed approaches to address these weaknesses.


Automated synthesis of reliable and efficient systems through game theory: a case study

Abstract: Reactive computer systems bear inherent complexity due to continuous interactions with their environment. While this environment often proves to be uncontrollable, we still want to ensure that critical computer systems will not fail, no matter what they face. Examples are legion: railway traffic, power plants, plane navigation systems, etc. Formal verification of a system may ensure that it satisfies a given specification, but only applies to an already existing model of a system. In this work, we address the problem of synthesis: starting from a specification of the desired behavior, we show how to build a suitable system controller that will enforce this specification. In particular, we discuss recent developments of that approach for systems that must ensure Boolean behaviors (e.g., reachability, liveness) along with quantitative requirements over their execution (e.g., never drop out of fuel, ensure a suitable mean response time). We notably illustrate a powerful, practically useable algorithm for the automated synthesis of provably safe reactive systems.


Efficiently decoding strings from their shingles

Abstract: Determining whether an unordered collection of overlapping substrings (called shingles) can be uniquely decoded into a consistent string is a problem that lies within the foundation of a broad assortment of disciplines ranging from networking and information theory through cryptography and even genetic engineering and linguistics. We present three perspectives on this problem: a graph theoretic framework due to Pevzner, an automata theoretic approach from our previous work, and a new insight that yields a time-optimal streaming algorithm for determining whether a string of $n$ characters over the alphabet $\Sigma$ can be uniquely decoded from its two-character shingles. Our algorithm achieves an overall time complexity $\Theta(n)$ and space complexity $O(|\Sigma|)$. As an application, we demonstrate how this algorithm can be extended to larger shingles for efficient string reconciliation.


Ubiquitous Indoor Localization and Worldwide Automatic Construction of Floor Plans

Abstract: Although GPS has been considered a ubiquitous outdoor localization technology, we are still far from a similar technology for indoor environments. While a number of technologies have been proposed for indoor localization, they are isolated efforts that are way from a true ubiquitous localization system. A ubiquitous indoor positioning system is envisioned to be deployed on a large scale worldwide, with minimum overhead, to work with heterogeneous devices, and to allow users to roam seamlessly from indoor to outdoor environments. Such a system will enable a wide set of applications including worldwide seamless direction finding between indoor locations, enhancing first responders' safety by providing anywhere localization and floor plans, and providing a richer environment for location-aware social networking applications. We describe an architecture for the ubiquitous indoor positioning system (IPS) and the challenges that have to be addressed to materialize it. We then focus on the feasibility of automating the construction of a worldwide indoor floor plan and fingerprint database which, as we believe, is one of the main challenges that limit the existence of a ubiquitous IPS system. Our proof of concept uses a crowd-sourcing approach that leverages the embedded sensors in today's cell phones as a worldwide distributed floor plan generation tool. This includes constructing the floor plans and determining the areas of interest (corridors, offices, meeting rooms, elevators, etc). The cloud computing concepts are also adopted for the processing and storage of the huge amount of data generated and requested by the system's users. Our results show the ability of the system to construct an accurate floor plan and identify the areas of interest with more than 90% accuracy. We also identify different research directions for addressing the challenges of realizing a true ubiquitous IPS system.


What "Crowdsourcing" Obscures: Exposing the Dynamics of Connected Crowd Work during Disaster

Abstract: The aim of this paper is to demonstrate that the current understanding of crowdsourcing may not be broad enough to capture the diversity of crowd work during disasters, or specific enough to highlight the unique dynamics of information organizing by the crowd in that context. In making this argument, this paper first unpacks the crowdsourcing term, examining its roots in open source development and outsourcing business models, and tying it to related concepts of human computation and collective intelligence. The paper then attempts to characterize several examples of crowd work during disasters using current definitions of crowdsourcing and existing models for human computation and collective intelligence, exposing a need for future research towards a framework for understanding crowd work.


Broadcast Search in Innovation Contests: Case for Hybrid Models

Abstract: Organizations use broadcast search to identify new avenues of innovation. Research on innovation contests provides insights on why excellent ideas are created in a broadcast search. However, there is little research on how excellent ideas are selected. Drawing from the brainstorming literature we find that the selection of excellent ideas needs further investigation. We propose that a hybrid model may lead to selection of better ideas. The hybrid model is a broadcast search approach that exploits the strengths of different actors and procedures in idea generation and the selection phase.


Symmetry Breaking Constraints: Recent Results

Abstract: Symmetry is an important problem in many combinatorial problems. One way of dealing with symmetry is to add constraints that eliminate symmetric solutions. We survey recent results in this area, focusing especially on two common and useful cases: symmetry breaking constraints for row and column symmetry, and symmetry breaking constraints for eliminating value symmetry


Collaborative Development in Wikipedia

Abstract: Using 16,068 articles in Wikipedia's Medicine Wikiproject, we study the relationship between collaboration and quality. We assess whether certain collaborative patterns are associated with information quality in terms of self-evaluated quality and article viewership. We find that the number of contributors has a curvilinear relationship to information quality, more contributors improving quality but only up to a certain point. Other articles that its collaborators work on also influences the quality of an information artifact, creating an interdependent network of artifacts and contributors. Finally, we see evidence of a recursive relationship between information quality and contributor activity, but that this recursive relationship attenuates over time.


Collective Cognitive Authority: Expertise Location via Social Labeling

Abstract: The problem of knowing who knows what is multi-faceted. Knowledge and expertise lie on a spectrum and one's expertise in one topic area may have little bearing on one's knowledge in a disparate topic area. In addition, we continue to learn new things over time. Each of us see but a sliver of our acquaintances' and co-workers' areas of expertise. By making explicit and visible many individual perceptions of cognitive authority, this work shows that a group can know what its members know about in a relatively efficient and inexpensive manner.


Event based classification of Web 2.0 text streams

Abstract: Web 2.0 applications like Twitter or Facebook create a continuous stream of information. This demands new ways of analysis in order to offer insight into this stream right at the moment of the creation of the information, because lots of this data is only relevant within a short period of time. To address this problem real time search engines have recently received increased attention. They take into account the continuous flow of information differently than traditional web search by incorporating temporal and social features, that describe the context of the information during its creation. Standard approaches where data first get stored and then is processed from a peristent storage suffer from latency. We want to address the fluent and rapid nature of text stream by providing an event based approach that analyses directly the stream of information. In a first step we want to define the difference between real time search and traditional search to clarify the demands in modern text filtering. In a second step we want to show how event based features can be used to support the tasks of real time search engines. Using the example of Twitter we present in this paper a way how to combine an event based approach with text mining and information filtering concepts in order to classify incoming information based on stream features. We calculate stream dependant features and feed them into a neural network in order to classify the text streams. We show the separative capabilities of event based features as the foundation for a real time search engine.


Crowdsourcing Gaze Data Collection

Abstract: Knowing where people look is a useful tool in many various image and video applications. However, traditional gaze tracking hardware is expensive and requires local study participants, so acquiring gaze location data from a large number of participants is very problematic. In this work we propose a crowdsourced method for acquisition of gaze direction data from a virtually unlimited number of participants, using a robust self-reporting mechanism (see Figure 1). Our system collects temporally sparse but spatially dense points-of-attention in any visual information. We apply our approach to an existing video data set and demonstrate that we obtain results similar to traditional gaze tracking. We also explore the parameter ranges of our method, and collect gaze tracking data for a large set of YouTube videos.


Blind graph rewriting systems

Abstract: We consider a simple (probably, the simplest) structure for random access memory. This structure can be used to construct a universal system with nearly void processor, namely, we demonstrate that the processor of such a system may have empty instruction set, in a more strong manner than the existing ZISC (zero instruction set computer based on ideas for artificial neural networks) and NISC architecture (no instruction set computing). More precisely, the processor will be forbidden to analyze any information stored in the memory, the latter being the only state of such a machine. This particular paper is to cover an isolated aspect of the idea, specifically, to provide the logical operations embedded into a system without any built-in conditional statements.


Galaxysearch - Discovering the Knowledge of Many by Using Wikipedia as a Meta-Searchindex

Abstract: We propose a dynamic map of knowledge generated from Wikipedia pages and the Web URLs contained therein. GalaxySearch provides answers to the questions we don't know how to ask, by constructing a semantic network of the most relevant pages in Wikipedia related to a search term. This search graph is constructed based on the Wikipedia bidirectional link structure, the most recent edits on the pages, the importance of the page, and the article quality; search results are then ranked by the centrality of their network position. GalaxySearch provides the results in three related ways: (1) WikiSearch - identifying the most prominent Wikipedia pages and Weblinks for a chosen topic, (2) WikiMap - creating a visual temporal map of the changes in the semantic network generated by the search results over the lifetime of the returned Wikipedia articles, and (3) WikiPulse - finding the most recent and most relevant changes and updates about a topic.


Collective Intelligence in Humans: A Literature Review

Abstract: This literature review focuses on collective intelligence in humans. A keyword search was performed on the Web of Knowledge and selected papers were reviewed in order to reveal themes relevant to collective intelligence. Three levels of abstraction were identified in discussion about the phenomenon: the micro-level, the macro-level and the level of emergence. Recurring themes in the literature were categorized under the above-mentioned framework and directions for future research were identified.


Airborne software tests on a fully virtual platform

Abstract: This paper presents the early deployment of a fully virtual platform to perform the tests of certified airborne software. This is an alternative to the current approach based on the use of dedicated hardware platforms.


Testing Formula Satisfaction

Abstract: We study the query complexity of testing for properties defined by read once formulas, as instances of {\em massively parametrized properties}, and prove several testability and non-testability results. First we prove the testability of any property accepted by a Boolean read-once formula involving any bounded arity gates, with a number of queries exponential in $\epsilon$, doubly exponential in the arity, and independent of all other parameters. When the gates are limited to being monotone, we prove that there is an {\em estimation} algorithm, that outputs an approximation of the distance of the input from satisfying the property. For formulas only involving And/Or gates, we provide a more efficient test whose query complexity is only quasipolynomial in $\epsilon$. On the other hand, we show that such testability results do not hold in general for formulas over non-Boolean alphabets; specifically we construct a property defined by a read-once arity $2$ (non-Boolean) formula over an alphabet of size $4$, such that any $1/4$-test for it requires a number of queries depending on the formula size. We also present such a formula over an alphabet of size $5$ that additionally satisfies a strong monotonicity condition.


Green Cellular Network Deployment To Reduce RF Pollution

Abstract: As the mobile telecommunication systems are growing tremendously all over the world, the numbers of handheld and base stations are also rapidly growing and it became very popular to see these base stations distributed everywhere in the neighborhood and on roof tops which has caused a considerable amount of panic to the public in Palestine concerning wither the radiated electromagnetic fields from these base stations may cause any health effect or hazard. Recently UP High Court in India ordered for removal of BTS towers from residential area, it has created panic among cellular communication network designers too. Green cellular networks could be a solution for the above problem. This paper deals with green cellular networks with the help of multi-layer overlaid hierarchical structure (macro / micro / pico / femto cells). Macrocell for area coverage, micro for pedestrian and a slow moving traffic while pico for indoor use and femto for individual high capacity users. This could be the answer of the problem of energy conservation and enhancement of spectral density also.


Converging to the Chase - a Tool for Finite Controllability

Abstract: We solve a problem, stated in [CGP10], showing that Sticky Datalog, defined in the cited paper as an element of the Datalog\pm project, has the finite controllability property. In order to do that, we develop a technique, which we believe can have further applications, of approximating Chase(D, T), for a database instance D and some sets of tuple generating dependencies T, by an infinite sequence of finite structures, all of them being models of T.


Explaining Adaptation in Genetic Algorithms With Uniform Crossover: The Hyperclimbing Hypothesis

Abstract: The hyperclimbing hypothesis is a hypothetical explanation for adaptation in genetic algorithms with uniform crossover (UGAs). Hyperclimbing is an intuitive, general-purpose, non-local search heuristic applicable to discrete product spaces with rugged or stochastic cost functions. The strength of this heuristic lie in its insusceptibility to local optima when the cost function is deterministic, and its tolerance for noise when the cost function is stochastic. Hyperclimbing works by decimating a search space, i.e. by iteratively fixing the values of small numbers of variables. The hyperclimbing hypothesis holds that UGAs work by implementing efficient hyperclimbing. Proof of concept for this hypothesis comes from the use of a novel analytic technique involving the exploitation of algorithmic symmetry. We have also obtained experimental results that show that a simple tweak inspired by the hyperclimbing hypothesis dramatically improves the performance of a UGA on large, random instances of MAX-3SAT and the Sherrington Kirkpatrick Spin Glasses problem.


Towards Understanding the Fundamentals of Mobility in Cellular Networks

Abstract: Despite the central role of mobility in wireless networks, analytical study on its impact on network performance is notoriously difficult. This paper aims to address this gap by proposing a random waypoint (RWP) mobility model defined on the entire plane and applying it to analyze two key cellular network parameters: handover rate and sojourn time. We first analyze the stochastic properties of the proposed model and compare it to two other models: the classical RWP mobility model and a synthetic truncated Levy walk model which is constructed from real mobility trajectories. The comparison shows that the proposed RWP mobility model is more appropriate for the mobility simulation in emerging cellular networks, which have ever-smaller cells. Then we apply the proposed model to cellular networks under both deterministic (hexagonal) and random (Poisson) base station (BS) models. We present analytic expressions for both handover rate and sojourn time, which have the expected property that the handover rate is proportional to the square root of BS density. Compared to an actual BS distribution, we find that the Poisson-Voronoi model is about as accurate in terms of mobility evaluation as hexagonal model, though being more pessimistic in that it predicts a higher handover rate and lower sojourn time.


Cloudpress 2.0: A MapReduce Approach for News Retrieval on the Cloud

Abstract: In this era of the Internet, the amount of news articles added every minute of everyday is humongous. As a result of this explosive amount of news articles, news retrieval systems are required to process the news articles frequently and intensively. The news retrieval systems that are in-use today are not capable of coping up with these data-intensive computations. Cloudpress 2.0 presented here, is designed and implemented to be scalable, robust and fault tolerant. It is designed in such a way that, all the processes involved in news retrieval such as fetching, pre-processing, indexing, storing and summarizing, exploit MapReduce paradigm and use the power of the Cloud computing. It uses novel approaches for parallel processing, for storing the news articles in a distributed database and for visualizing them as a 3D visual. It uses Lucene-based indexing for efficient and faster retrieval. It also includes a novel query expansion feature for searching the news articles. Cloudpress 2.0 also allows on-the-fly, extractive summarization of news articles based on the input query.


Crowdsourcing Collective Emotional Intelligence

Abstract: One of the hallmarks of emotional intelligence is the ability to regulate emotions. Research suggests that cognitive reappraisal - a technique that involves reinterpreting the meaning of a thought or situation - can down-regulate negative emotions, without incurring significant psychological or physiological costs. Habitual use of this strategy is also linked to many key indices of physical and emotional health. Unfortunately, this technique is not always easy to apply. Thinking flexibly about stressful thoughts and situations requires creativity and poise, faculties that often elude us when we need them the most. In this paper, we propose an assistive technology that coordinates collective intelligence on demand, to help individuals reappraise stressful thoughts and situations. In two experiments, we assess key features of our design and we demonstrate the feasibility of crowdsourcing empathetic reappraisals with on demand workforces, such as Amazon's Mechanical Turk.


Efficient sub-5 approximations for minimum dominating sets in unit disk graphs

Abstract: A unit disk graph is the intersection graph of n congruent disks in the plane. Dominating sets in unit disk graphs are widely studied due to their application in wireless ad-hoc networks. Because the minimum dominating set problem for unit disk graphs is NP-hard, numerous approximation algorithms have been proposed in the literature, including some PTAS. However, since the proposal of a linear-time 5-approximation algorithm in 1995, the lack of efficient algorithms attaining better approximation factors has aroused attention. We introduce a linear-time O(n+m) approximation algorithm that takes the usual adjacency representation of the graph as input and outputs a 44/9-approximation. This approximation factor is also attained by a second algorithm, which takes the geometric representation of the graph as input and runs in O(n log n) time regardless of the number of edges. Additionally, we propose a 43/9-approximation which can be obtained in O(n^2 m) time given only the graph's adjacency representation. It is noteworthy that the dominating sets obtained by our algorithms are also independent sets.


Rationale awareness for quality assurance in iterative human computation processes

Abstract: Human computation refers to the outsourcing of computation tasks to human workers. It offers a new direction for solving a variety of problems and calls for innovative ways of managing human computation processes. The majority of human computation tasks take a parallel approach, whereas the potential of an iterative approach, i.e., having workers iteratively build on each other's work, has not been sufficiently explored. This study investigates whether and how human workers' awareness of previous workers' rationales affects the performance of the iterative approach in a brainstorming task and a rating task. Rather than viewing this work as a conclusive piece, the author believes that this research endeavor is just the beginning of a new research focus that examines and supports meta-cognitive processes in crowdsourcing activities.


Optimizing Scrip Systems: Crashes, Altruists, Hoarders, Sybils and Collusion

Abstract: Scrip, or artificial currency, is a useful tool for designing systems that are robust to selfish behavior by users. However, it also introduces problems for a system designer, such as how the amount of money in the system should be set. In this paper, the effect of varying the total amount of money in a scrip system on efficiency (i.e., social welfare---the total utility of all the agents in the system) is analyzed, and it is shown that by maintaining the appropriate ratio between the total amount of money and the number of agents, efficiency is maximized. This ratio can be found by increasing the money supply to just below the point that the system would experience a "monetary crash," where money is sufficiently devalued that no agent is willing to perform a service. The implications of the presence of altruists, hoarders, sybils, and collusion on the performance of the system are examined. Approaches are discussed to identify the strategies and types of agents.


No big deal: introducing roles to reduce the size of ATL models

Abstract: In the following paper we present a new semantics for the well-known strategic logic ATL. It is based on adding roles to concurrent game structures, that is at every state, each agent belongs to exactly one role, and the role specifies what actions are available to him at that state. We show advantages of the new semantics, analyze model checking complexity and prove equivalence between standard ATL semantics and our new approach.


Crowd & Prejudice: An Impossibility Theorem for Crowd Labelling without a Gold Standard

Abstract: A common use of crowd sourcing is to obtain labels for a dataset. Several algorithms have been proposed to identify uninformative members of the crowd so that their labels can be disregarded and the cost of paying them avoided. One common motivation of these algorithms is to try and do without any initial set of trusted labeled data. We analyse this class of algorithms as mechanisms in a game-theoretic setting to understand the incentives they create for workers. We find an impossibility result that without any ground truth, and when workers have access to commonly shared 'prejudices' upon which they agree but are not informative of true labels, there is always equilibria where all agents report the prejudice. A small amount amount of gold standard data is found to be sufficient to rule out these equilibria.


Delta-Complete Decision Procedures for Satisfiability over the Reals

Abstract: We introduce the notion of "\delta-complete decision procedures" for solving SMT problems over the real numbers, with the aim of handling a wide range of nonlinear functions including transcendental functions and solutions of Lipschitz-continuous ODEs. Given an SMT problem \varphi and a positive rational number \delta, a \delta-complete decision procedure determines either that \varphi is unsatisfiable, or that the "\delta-weakening" of \varphi is satisfiable. Here, the \delta-weakening of \varphi is a variant of \varphi that allows \delta-bounded numerical perturbations on \varphi. We prove the existence of \delta-complete decision procedures for bounded SMT over reals with functions mentioned above. For functions in Type 2 complexity class C, under mild assumptions, the bounded \delta-SMT problem is in NP^C. \delta-Complete decision procedures can exploit scalable numerical methods for handling nonlinearity, and we propose to use this notion as an ideal requirement for numerically-driven decision procedures. As a concrete example, we formally analyze the DPLL<ICP> framework, which integrates Interval Constraint Propagation (ICP) in DPLL(T), and establish necessary and sufficient conditions for its \delta-completeness. We discuss practical applications of \delta-complete decision procedures for correctness-critical applications including formal verification and theorem proving.


Distributed Learning, Communication Complexity and Privacy

Abstract: We consider the problem of PAC-learning from distributed data and analyze fundamental communication complexity questions involved. We provide general upper and lower bounds on the amount of communication needed to learn well, showing that in addition to VC-dimension and covering number, quantities such as the teaching-dimension and mistake-bound of a class play an important role. We also present tight results for a number of common concept classes including conjunctions, parity functions, and decision lists. For linear separators, we show that for non-concentrated distributions, we can use a version of the Perceptron algorithm to learn with much less communication than the number of updates given by the usual margin bound. We also show how boosting can be performed in a generic manner in the distributed setting to achieve communication with only logarithmic dependence on 1/epsilon for any concept class, and demonstrate how recent work on agnostic learning from class-conditional queries can be used to achieve low communication in agnostic settings as well. We additionally present an analysis of privacy, considering both differential privacy and a notion of distributional privacy that is especially appealing in this context.


When majority voting fails: Comparing quality assurance methods for noisy human computation environment

Abstract: Quality assurance remains a key topic in human computation research. Prior work indicates that majority voting is effective for low difficulty tasks, but has limitations for harder tasks. This paper explores two methods of addressing this problem: tournament selection and elimination selection, which exploit 2-, 3- and 4-way comparisons between different answers to human computation tasks. Our experimental results and statistical analyses show that both methods produce the correct answer in noisy human computation environment more often than majority voting. Furthermore, we find that the use of 4-way comparisons can significantly reduce the cost of quality assurance relative to the use of 2-way comparisons.


Hardness Results for Approximate Pure Horn CNF Formulae Minimization

Abstract: We study the hardness of approximation of clause minimum and literal minimum representations of pure Horn functions in $n$ Boolean variables. We show that unless P=NP, it is not possible to approximate in polynomial time the minimum number of clauses and the minimum number of literals of pure Horn CNF representations to within a factor of $2^{\log^{1-o(1)} n}$. This is the case even when the inputs are restricted to pure Horn 3-CNFs with $O(n^{1+\varepsilon})$ clauses, for some small positive constant $\varepsilon$. Furthermore, we show that even allowing sub-exponential time computation, it is still not possible to obtain constant factor approximations for such problems unless the Exponential Time Hypothesis turns out to be false.


Functional Magnetic Resonance Imaging and the Challenge of Balancing Human Security with State Security

Abstract: Recent reports reveal that violent extremists are trying to obtain insider positions that may increase the impact of any attack on critical infrastructure and could potentially endanger state services, people's lives and even democracy. It is of utmost importance to be able to adopt extreme security measures in certain high-risk situations in order to secure critical infrastructure and thus lower the level of terrorist threats while preserving the rights of citizens. To counter these threats, our research is aiming for extreme measures to analyse and evaluate human threats related assessment methods for employee screening and evaluations using cognitive analysis technology, in particular functional Magnetic Resonance Imaging (fMRI). The development of fMRI has led some researchers to conclude that this technology has forensic potential and may be useful in investing personality traits, mental illness, psychopathology, racial prejudice and religious extremism. However, critics claim that this technology may present many new human rights and ethical dilemmas and could result in potentially disastrous outcomes. The main thrust of the research is to counter above concerns and harmful consequences by presenting a set of ethical and professional guidelines that will substantially reduce the risk of unethical use of this technology. The significance of this research is to ensure the limits of the state/organisation's right to peer into an individual's thought process with and without consent, to define the parameters of a person's right to ensure that fMRI scans do not pose more than an appropriate threat to cognitive liberty, and the proper use of such information in civil, forensic and security settings.


An Algorithmic Solution for Computing Circle Intersection Areas and its Applications to Wireless Communications

Abstract: A novel iterative algorithm for the efficient computation of the intersection areas of an arbitrary number of circles is presented. The algorithm, based on a trellis-structure, hinges on two geometric results which allow the existence-check and the computation of the area of the intersection regions generated by more than three circles by simple algebraic manipulations of the intersection areas of a smaller number of circles. The presented algorithm is a powerful tool for the performance analysis of wireless networks, and finds many applications, ranging from sensor to cellular networks. As an example of practical application, an insightful study of the uplink outage probability of in a wireless network with cooperative access points as a function of the transmission power and access point density is presented.