Fault Tolerant Boolean Satisfiability

Abstract: A delta-model is a satisfying assignment of a Boolean formula for which any small alteration, such as a single bit flip, can be repaired by flips to some small number of other bits, yielding a new satisfying assignment. These satisfying assignments represent robust solutions to optimization problems (e.g., scheduling) where it is possible to recover from unforeseen events (e.g., a resource becoming unavailable). The concept of delta-models was introduced by Ginsberg, Parkes and Roy (AAAI 1998), where it was proved that finding delta-models for general Boolean formulas is NP-complete. In this paper, we extend that result by studying the complexity of finding delta-models for classes of Boolean formulas which are known to have polynomial time satisfiability solvers. In particular, we examine 2-SAT, Horn-SAT, Affine-SAT, dual-Horn-SAT, 0-valid and 1-valid SAT. We see a wide variation in the complexity of finding delta-models, e.g., while 2-SAT and Affine-SAT have polynomial time tests for delta-models, testing whether a Horn-SAT formula has one is NP-complete.


Cognitive Principles in Robust Multimodal Interpretation

Abstract: Multimodal conversational interfaces provide a natural means for users to communicate with computer systems through multiple modalities such as speech and gesture. To build effective multimodal interfaces, automated interpretation of user multimodal inputs is important. Inspired by the previous investigation on cognitive status in multimodal human machine interaction, we have developed a greedy algorithm for interpreting user referring expressions (i.e., multimodal reference resolution). This algorithm incorporates the cognitive principles of Conversational Implicature and Givenness Hierarchy and applies constraints from various sources (e.g., temporal, semantic, and contextual) to resolve references. Our empirical results have shown the advantage of this algorithm in efficiently resolving a variety of user references. Because of its simplicity and generality, this approach has the potential to improve the robustness of multimodal input interpretation.


How to prolong network life-span in wireless networks

Abstract: One of the most important problems in wireless sensor network is to develop a routing protocol that has energy efficiency. Since the power of the sensor Nodes are limited, conserving energy and network life is a critical issue in wireless sensor network. Clustering is one of the known methods widely used to face these challenges. In this paper, a cluster based communication protocol with considering the low energy consumption in wireless sensor networks, is introduced which balances the energy load among sensor nodes. The nodes close to each other have more overlap; they sense the same data from environment and cause a waste of energy by generating repetitive data. In this paper, a cluster based routing protocol is introduced, in the proposed protocol, in each round a certain number of nodes are specified; the nodes which have at least one neighboring node at a distance less than the threshold. Then, among them the nodes with less energy and greater overlap with their neighbors have been chosen to go to sleep mode, Also, the energy imbalance among sensor nodes is reduced by integrating the distance of the nodes from the base station into clustering policies.


Memetic Algorithms: Parametrization and Balancing Local and Global Search

Abstract: This is a preprint of a book chapter from the Handbook of Memetic Algorithms, Studies in Computational Intelligence, Vol. 379, ISBN 978-3-642-23246-6, Springer, edited by F. Neri, C. Cotta, and P. Moscato. It is devoted to the parametrization of memetic algorithms and how to find a good balance between global and local search.


ABHIVYAKTI: A Vision Based Intelligent System for Elder and Sick Persons

Abstract: This paper describes an intelligent system ABHIVYAKTI, which would be pervasive in nature and based on the Computer Vision. It would be very easy in use and deployment. Elder and sick people who are not able to talk or walk, they are dependent on other human beings and need continuous monitoring, while our system provides flexibility to the sick or elder person to announce his or her need to their caretaker by just showing a particular gesture with the developed system, if the caretaker is not nearby. This system will use fingertip detection techniques for acquiring gesture and Artificial Neural Networks (ANNs) will be used for gesture recognition.


A Survey of Ocean Simulation and Rendering Techniques in Computer Graphics

Abstract: This paper presents a survey of ocean simulation and rendering methods in computer graphics. To model and animate the ocean's surface, these methods mainly rely on two main approaches: on the one hand, those which approximate ocean dynamics with parametric, spectral or hybrid models and use empirical laws from oceanographic research. We will see that this type of methods essentially allows the simulation of ocean scenes in the deep water domain, without breaking waves. On the other hand, physically-based methods use Navier-Stokes Equations (NSE) to represent breaking waves and more generally ocean surface near the shore. We also describe ocean rendering methods in computer graphics, with a special interest in the simulation of phenomena such as foam and spray, and light's interaction with the ocean surface.


An Empirical Study of UDP (CBR) Packet Performance over AODV Single & Multi-Channel Parallel Transmission in MANET

Abstract: Mobile Ad-hoc Network is a temporary network which is the cooperative engagement of a collection of standalone mobile nodes that are not connected to any external network. It is a decentralized network where mobile nodes can be easily deployed in almost any environment without sophisticated infrastructure support. An empirical study has been done for AODV routing protocol under single channel and multi channel environment using the tool NS2. To compare the performance of AODV in the two environments, the simulation results have been analyzed by graphical manner and trace file based on QoS metrics such as throughput, packet drop, delay and jitter. The simulation result analysis verifies the AODV routing protocol performances for single channel and multi channel. After the analysis of the simulation scenario we suggest that use of Parallel MAC (P-MAC) may enhance the performance for multi channel.


Interconnection network with a shared whiteboard: Impact of (a)synchronicity on computing power

Abstract: In this work we study the computational power of graph-based models of distributed computing in which each node additionally has access to a global whiteboard. A node can read the contents of the whiteboard and, when activated, can write one message of O(log n) bits on it. When the protocol terminates, each node computes the output based on the final contents of the whiteboard. We consider several scheduling schemes for nodes, providing a strict ordering of their power in terms of the problems which can be solved with exactly one activation per node. The problems used to separate the models are related to Maximal Independent Set, detection of cycles of length 4, and BFS spanning tree constructions.


Failure Filtrations for Fenced Sensor Networks

Abstract: In this paper we consider the question of sensor network coverage for a 2-dimensional domain. We seek to compute the probability that a set of sensors fails to cover given only non-metric, local (who is talking to whom) information and a probability distribution of failure of each node. This builds on the work of de Silva and Ghrist who analyzed this problem in the deterministic situation. We first show that a it is part of a slightly larger class of problems which is #P-complete, and thus fast algorithms likely do not exist unless P$=$NP. We then give a deterministic algorithm which is feasible in the case of a small set of sensors, and give a dynamic algorithm for an arbitrary set of sensors failing over time which utilizes a new criterion for coverage based on the one proposed by de Silva and Ghrist. These algorithms build on the theory of topological persistence.


Outsourcing Competence

Abstract: The topic of this paper, competences needed for outsourcing, is organized by first providing a generic competence scheme, which is subsequently instantiated to the area of sourcing and outsourcing. Sourcing and outsourcing are positioned as different areas of activity, neither one of which is subsumed under the other one. It is argued that competences relevant for outsourcing are mainly community based rather than evidence based. Subjective ability and objective ability are distinguished as categories, together making up ability, which are distinct but not necessarily disjoint from competence. Conjectural ability is introduced as a form of subjective ability. A person's competence profile includes competences as well as abilities, including subjective ones. Competence assessment and acquisition as well as the impact of assessed competence on practical work is described. The analysis of competence and ability thus developed is used as standpoint from which to extract a specification of an audience for a theory of outsourcing, yet to be written. Moreover, it allows to formulate requirements for and in preparation of the development of an outsourcing theory. Formulating these requirements is done under the assumption that a person's awareness of a theory of outsourcing is expected to strengthen that person's outsourcing competence profile.


Multiple-Goal Heuristic Search

Abstract: This paper presents a new framework for anytime heuristic search where the task is to achieve as many goals as possible within the allocated resources. We show the inadequacy of traditional distance-estimation heuristics for tasks of this type and present alternative heuristics that are more appropriate for multiple-goal search. In particular, we introduce the marginal-utility heuristic, which estimates the cost and the benefit of exploring a subtree below a search node. We developed two methods for online learning of the marginal-utility heuristic. One is based on local similarity of the partial marginal utility of sibling nodes, and the other generalizes marginal-utility over the state feature space. We apply our adaptive and non-adaptive multiple-goal search algorithms to several problems, including focused crawling, and show their superiority over existing methods.


FluCaP: A Heuristic Search Planner for First-Order MDPs

Abstract: We present a heuristic search algorithm for solving first-order Markov Decision Processes (FOMDPs). Our approach combines first-order state abstraction that avoids evaluating states individually, and heuristic search that avoids evaluating all states. Firstly, in contrast to existing systems, which start with propositionalizing the FOMDP and then perform state abstraction on its propositionalized version we apply state abstraction directly on the FOMDP avoiding propositionalization. This kind of abstraction is referred to as first-order state abstraction. Secondly, guided by an admissible heuristic, the search is restricted to those states that are reachable from the initial state. We demonstrate the usefulness of the above techniques for solving FOMDPs with a system, referred to as FluCaP (formerly, FCPlanner), that entered the probabilistic track of the 2004 International Planning Competition (IPC2004) and demonstrated an advantage over other planners on the problems represented in first-order terms.


Towards an interoperable information infrastructure providing decision support for genomic medicine

Abstract: Genetic dispositions play a major role in individual disease risk and treatment response. Genomic medicine, in which medical decisions are refined by genetic information of particular patients, is becoming increasingly important. Here we describe our work and future visions around the creation of a distributed infrastructure for pharmacogenetic data and medical decision support, based on industry standards such as the Web Ontology Language (OWL) and the Arden Syntax.


The Statistical Inefficiency of Sparse Coding for Images (or, One Gabor to Rule them All)

Abstract: Sparse coding is a proven principle for learning compact representations of images. However, sparse coding by itself often leads to very redundant dictionaries. With images, this often takes the form of similar edge detectors which are replicated many times at various positions, scales and orientations. An immediate consequence of this observation is that the estimation of the dictionary components is not statistically efficient. We propose a factored model in which factors of variation (e.g. position, scale and orientation) are untangled from the underlying Gabor-like filters. There is so much redundancy in sparse codes for natural images that our model requires only a single dictionary element (a Gabor-like edge detector) to outperform standard sparse coding. Our model scales naturally to arbitrary-sized images while achieving much greater statistical efficiency during learning. We validate this claim with a number of experiments showing, in part, superior compression of out-of-sample data using a sparse coding dictionary learned with only a single image.


Optimal Eviction Policies for Stochastic Address Traces

Abstract: The eviction problem for memory hierarchies is studied for the Hidden Markov Reference Model (HMRM) of the memory trace, showing how miss minimization can be naturally formulated in the optimal control setting. In addition to the traditional version assuming a buffer of fixed capacity, a relaxed version is also considered, in which buffer occupancy can vary and its average is constrained. Resorting to multiobjective optimization, viewing occupancy as a cost rather than as a constraint, the optimal eviction policy is obtained by composing solutions for the individual addressable items. This approach is then specialized to the Least Recently Used Stack Model (LRUSM), a type of HMRM often considered for traces, which includes V-1 parameters, where V is the size of the virtual space. A gain optimal policy for any target average occupancy is obtained which (i) is computable in time O(V) from the model parameters, (ii) is optimal also for the fixed capacity case, and (iii) is characterized in terms of priorities, with the name of Least Profit Rate (LPR) policy. An O(log C) upper bound (being C the buffer capacity) is derived for the ratio between the expected miss rate of LPR and that of OPT, the optimal off-line policy; the upper bound is tightened to O(1), under reasonable constraints on the LRUSM parameters. Using the stack-distance framework, an algorithm is developed to compute the number of misses incurred by LPR on a given input trace, simultaneously for all buffer capacities, in time O(log V) per access. Finally, some results are provided for miss minimization over a finite horizon and over an infinite horizon under bias optimality, a criterion more stringent than gain optimality.


Sound Analysis and Synthesis Adaptive in Time and Two Frequency Bands

Abstract: We present an algorithm for sound analysis and resynthesis with local automatic adaptation of time-frequency resolution. There exists several algorithms allowing to adapt the analysis window depending on its time or frequency location; in what follows we propose a method which select the optimal resolution depending on both time and frequency. We consider an approach that we denote as analysis-weighting, from the point of view of Gabor frame theory. We analyze in particular the case of different adaptive time-varying resolutions within two complementary frequency bands; this is a typical case where perfect signal reconstruction cannot in general be achieved with fast algorithms, causing a certain error to be minimized. We provide examples of adaptive analyses of a music sound, and outline several possibilities that this work opens.


A Fuzzy Co-Clustering approach for Clickstream Data Pattern

Abstract: Web Usage mining is a very important tool to extract the hidden business intelligence data from large databases. The extracted information provides the organizations with the ability to produce results more effectively to improve their businesses and increasing of sales. Co-clustering is a powerful bipartition technique which identifies group of users associated to group of web pages. These associations are quantified to reveal the users' interest in the different web pages' clusters. In this paper, Fuzzy Co-Clustering algorithm is proposed for clickstream data to identify the subset of users of similar navigational behavior /interest over a subset of web pages of a website. Targeting the users group for various promotional activities is an important aspect of marketing practices. Experiments are conducted on real dataset to prove the efficiency of proposed algorithm. The results and findings of this algorithm could be used to enhance the marketing strategy for directing marketing, advertisements for web based businesses and so on.


SLALOM: a Language for SLA specification and monitoring

Abstract: IT services provisioning is usually underpinned by service level agreements (SLAs), aimed at guaranteeing services quality. However, there is a gap between the customer perspective (business oriented) and that of the service provider (implementation oriented) that becomes more evident while defining and monitoring SLAs. This paper proposes a domain specific language (SLA Language for specificatiOn and Monitoring - SLALOM) to bridge the previous gap. The first step in SLALOM creation was factoring out common concepts, by composing the BPMN metamodel with that of the SLA life cycle, as described in ITIL. The derived metamodel expresses the SLALOM abstract syntax model. The second step was to write concrete syntaxes targeting different aims, such as SLA representation in process models. An example of SLALOM's concrete syntax model instantiation for an IT service sup-ported by self-service financial terminals is presented.


On the relation between Differential Privacy and Quantitative Information Flow

Abstract: Differential privacy is a notion that has emerged in the community of statistical databases, as a response to the problem of protecting the privacy of the database's participants when performing statistical queries. The idea is that a randomized query satisfies differential privacy if the likelihood of obtaining a certain answer for a database $x$ is not too different from the likelihood of obtaining the same answer on adjacent databases, i.e. databases which differ from $x$ for only one individual. Information flow is an area of Security concerned with the problem of controlling the leakage of confidential information in programs and protocols. Nowadays, one of the most established approaches to quantify and to reason about leakage is based on the R\'enyi min entropy version of information theory. In this paper, we analyze critically the notion of differential privacy in light of the conceptual framework provided by the R\'enyi min information theory. We show that there is a close relation between differential privacy and leakage, due to the graph symmetries induced by the adjacency relation. Furthermore, we consider the utility of the randomized answer, which measures its expected degree of accuracy. We focus on certain kinds of utility functions called "binary", which have a close correspondence with the R\'enyi min mutual information. Again, it turns out that there can be a tight correspondence between differential privacy and utility, depending on the symmetries induced by the adjacency relation and by the query. Depending on these symmetries we can also build an optimal-utility randomization mechanism while preserving the required level of differential privacy. Our main contribution is a study of the kind of structures that can be induced by the adjacency relation and the query, and how to use them to derive bounds on the leakage and achieve the optimal utility.


Do Software Languages Engineers Evaluate their Languages?

Abstract: Domain Specific Languages (DSLs) can contribute to increment productivity, while reducing the required maintenance and programming expertise. We hypothesize that Software Languages Engineering (SLE) developers consistently skip, or relax, Language Evaluation. Based on the experience of engineering other types of software products, we assume that this may potentially lead to the deployment of inadequate languages. The fact that the languages already deal with concepts from the problem domain, and not the solution domain, is not enough to validate several issues at stake, such as its expressiveness, usability, effectiveness, maintainability, or even the domain expert's productivity while using them. We present a systematic review on articles published in top ranked venues, from 2001 to 2008, which report DSLs' construction, to characterize the common practice. This work confirms our initial hypothesis and lays the ground for the discussion on how to include a systematic approach to DSL evaluation in the SLE process.


An overview of metrics-based approaches to support software components reusability assessment

Abstract: Objective: To present an overview on the current state of the art concerning metrics-based quality evaluation of software components and component assemblies. Method: Comparison of several approaches available in the literature, using a framework comprising several aspects, such as scope, intent, definition technique, and maturity. Results: The identification of common shortcomings of current approaches, such as ambiguity in definition, lack of adequacy of the specifying formalisms and insufficient validation of current quality models and metrics for software components. Conclusions: Quality evaluation of components and component-based infrastructures presents new challenges to the Experimental Software Engineering community.


Distributed Air Traffic Control : A Human Safety Perspective

Abstract: The issues in air traffic control have so far been addressed with the intent to improve resource utilization and achieve an optimized solution with respect to fuel comsumption of aircrafts, efficient usage of the available airspace with minimal congestion related losses under various dynamic constraints. So the focus has almost always been more on smarter management of traffic to increase profits while human safety, though achieved in the process, we believe, has remained less seriously attended. This has become all the more important given that we have overburdened and overstressed air traffic controllers managing hundreds of airports and thousands of aircrafts per day. We propose a multiagent system based distributed approach to handle air traffic ensuring complete human (passenger) safety without removing any humans (ground controllers) from the loop thereby also retaining the earlier advantages in the new solution. The detailed design of the agent system, which will be easily interfacable with the existing environment, is described. Based on our initial findings from simulations, we strongly believe the system to be capable of handling the nuances involved, to be extendable and customizable at any later point in time.


A Novel comprehensive method for real time Video Motion Detection Surveillance

Abstract: This article describes a comprehensive system for surveillance and monitoring applications. The development of an efficient real time video motion detection system is motivated by their potential for deployment in the areas where security is the main concern. The paper presents a platform for real time video motion detection and subsequent generation of an alarm condition as set by the parameters of the control system. The prototype consists of a mobile platform mounted with RF camera which provides continuous feedback of the environment. The received visual information is then analyzed by user for appropriate control action, thus enabling the user to operate the system from a remote location. The system is also equipped with the ability to process the image of an object and generate control signals which are automatically transmitted to the mobile platform to track the object.


Learning Dependency-Based Compositional Semantics

Abstract: Suppose we want to build a system that answers a natural language question by representing its semantics as a logical form and computing the answer given a structured database of facts. The core part of such a system is the semantic parser that maps questions to logical forms. Semantic parsers are typically trained from examples of questions annotated with their target logical forms, but this type of annotation is expensive. Our goal is to learn a semantic parser from question-answer pairs instead, where the logical form is modeled as a latent variable. Motivated by this challenging learning problem, we develop a new semantic formalism, dependency-based compositional semantics (DCS), which has favorable linguistic, statistical, and computational properties. We define a log-linear distribution over DCS logical forms and estimate the parameters using a simple procedure that alternates between beam search and numerical optimization. On two standard semantic parsing benchmarks, our system outperforms all existing state-of-the-art systems, despite using no annotated logical forms.


Content-Aware Rate Control for Video Transmission with Buffer Constraints in Multipath Networks

Abstract: Being an integral part of the network traffic, nowadays it's vital to design robust mechanisms to provide QoS for multimedia applications. The main goal of this paper is to provide an efficient solution to support content-aware video transmission mechanism with buffer underflow avoidance at the receiver in multipath networks. Towards this, we introduce a content-aware time-varying utility function, where the quality impacts of video content is incorporated into its definition. Using the proposed utility function, we formulate a multipath Dynamic Network Utility Maximization (DNUM) problem for the rate allocation of video streams, where it takes into account QoS demand of video streams in terms of buffer underflow avoidance. Finally, using primal-dual method, we propose a distributed solution that optimally allocates the shared bandwidth to video streams. The numerical examples demonstrate the efficacy of the proposed content-aware rate allocation algorithm for video sources in both single and multiple path network models.


Video OCR for Video Indexing

Abstract: Video OCR is a technique that can greatly help to locate the topics of interest in video via the automatic extraction and reading of captions and annotations. Text in video can provide key indexing information. Recognizing such text for search application is critical. Major difficult problem for character recognition for videos is degraded and deformated characters, low resolution characters or very complex background. To tackle the problem preprocessing on text image plays vital role. Most of the OCR engines are working on the binary image so to find a better binarization procedure for image to get a desired result is important.Accurate binarization process minimizes the error rate of video OCR.


#h00t: Censorship Resistant Microblogging

Abstract: Microblogging services such as Twitter are an increasingly important way to communicate, both for individuals and for groups through the use of hashtags that denote topics of conversation. However, groups can be easily blocked from communicating through blocking of posts with the given hashtags. We propose #h00t, a system for censorship resistant microblogging. #h00t presents an interface that is much like Twitter, except that hashtags are replaced with very short hashes (e.g., 24 bits) of the group identifier. Naturally, with such short hashes, hashtags from different groups may collide and #h00t users will actually seek to create collisions. By encrypting all posts with keys derived from the group identifiers, #h00t client software can filter out other groups' posts while making such filtering difficult for the adversary. In essence, by leveraging collisions, groups can tunnel their posts in other groups' posts. A censor could not block a given group without also blocking the other groups with colliding hashtags. We evaluate the feasibility of #h00t through traces collected from Twitter, showing that a single modern computer has enough computational throughput to encrypt every tweet sent through Twitter in real time. We also use these traces to analyze the bandwidth and anonymity tradeoffs that would come with different variations on how group identifiers are encoded and hashtags are selected to purposefully collide with one another.


Explanation-Based Auditing

Abstract: To comply with emerging privacy laws and regulations, it has become common for applications like electronic health records systems (EHRs) to collect access logs, which record each time a user (e.g., a hospital employee) accesses a piece of sensitive data (e.g., a patient record). Using the access log, it is easy to answer simple queries (e.g., Who accessed Alice's medical record?), but this often does not provide enough information. In addition to learning who accessed their medical records, patients will likely want to understand why each access occurred. In this paper, we introduce the problem of generating explanations for individual records in an access log. The problem is motivated by user-centric auditing applications, and it also provides a novel approach to misuse detection. We develop a framework for modeling explanations which is based on a fundamental observation: For certain classes of databases, including EHRs, the reason for most data accesses can be inferred from data stored elsewhere in the database. For example, if Alice has an appointment with Dr. Dave, this information is stored in the database, and it explains why Dr. Dave looked at Alice's record. Large numbers of data accesses can be explained using general forms called explanation templates. Rather than requiring an administrator to manually specify explanation templates, we propose a set of algorithms for automatically discovering frequent templates from the database (i.e., those that explain a large number of accesses). We also propose techniques for inferring collaborative user groups, which can be used to enhance the quality of the discovered explanations. Finally, we have evaluated our proposed techniques using an access log and data from the University of Michigan Health System. Our results demonstrate that in practice we can provide explanations for over 94% of data accesses in the log.


Human-powered Sorts and Joins

Abstract: Crowdsourcing markets like Amazon's Mechanical Turk (MTurk) make it possible to task people with small jobs, such as labeling images or looking up phone numbers, via a programmatic interface. MTurk tasks for processing datasets with humans are currently designed with significant reimplementation of common workflows and ad-hoc selection of parameters such as price to pay per task. We describe how we have integrated crowds into a declarative workflow engine called Qurk to reduce the burden on workflow designers. In this paper, we focus on how to use humans to compare items for sorting and joining data, two of the most common operations in DBMSs. We describe our basic query interface and the user interface of the tasks we post to MTurk. We also propose a number of optimizations, including task batching, replacing pairwise comparisons with numerical ratings, and pre-filtering tables before joining them, which dramatically reduce the overall cost of running sorts and joins on the crowd. In an experiment joining two sets of images, we reduce the overall cost from $67 in a naive implementation to about $3, without substantially affecting accuracy or latency. In an end-to-end experiment, we reduced cost by a factor of 14.5.


Verifying Computations with Streaming Interactive Proofs

Abstract: When computation is outsourced, the data owner would like to be assured that the desired computation has been performed correctly by the service provider. In theory, proof systems can give the necessary assurance, but prior work is not sufficiently scalable or practical. In this paper, we develop new proof protocols for verifying computations which are streaming in nature: the verifier (data owner) needs only logarithmic space and a single pass over the input, and after observing the input follows a simple protocol with a prover (service provider) that takes logarithmic communication spread over a logarithmic number of rounds. These ensure that the computation is performed correctly: that the service provider has not made any errors or missed out some data. The guarantee is very strong: even if the service provider deliberately tries to cheat, there is only vanishingly small probability of doing so undetected, while a correct computation is always accepted. We first observe that some theoretical results can be modified to work with streaming verifiers, showing that there are efficient protocols for problems in the complexity classes NP and NC. Our main results then seek to bridge the gap between theory and practice by developing usable protocols for a variety of problems of central importance in streaming and database processing. All these problems require linear space in the traditional streaming model, and therefore our protocols demonstrate that adding a prover can exponentially reduce the effort needed by the verifier. Our experimental results show that our protocols are practical and scalable.


A MovingObject Index for Efficient Query Processing with Peer-Wise Location Privacy

Abstract: With the growing use of location-based services, location privacy attracts increasing attention from users, industry, and the research community. While considerable effort has been devoted to inventing techniques that prevent service providers from knowing a user's exact location, relatively little attention has been paid to enabling so-called peer-wise privacy--the protection of a user's location from unauthorized peer users. This paper identifies an important efficiency problem in existing peer-privacy approaches that simply apply a filtering step to identify users that are located in a query range, but that do not want to disclose their location to the querying peer. To solve this problem, we propose a novel, privacy-policy enabled index called the PEB-tree that seamlessly integrates location proximity and policy compatibility. We propose efficient algorithms that use the PEB-tree for processing privacy-aware range and kNN queries. Extensive experiments suggest that the PEB-tree enables efficient query processing.


ERA: Efficient Serial and Parallel Suffix Tree Construction for Very Long Strings

Abstract: The suffix tree is a data structure for indexing strings. It is used in a variety of applications such as bioinformatics, time series analysis, clustering, text editing and data compression. However, when the string and the resulting suffix tree are too large to fit into the main memory, most existing construction algorithms become very inefficient. This paper presents a disk-based suffix tree construction method, called Elastic Range (ERa), which works efficiently with very long strings that are much larger than the available memory. ERa partitions the tree construction process horizontally and vertically and minimizes I/Os by dynamically adjusting the horizontal partitions independently for each vertical partition, based on the evolving shape of the tree and the available memory. Where appropriate, ERa also groups vertical partitions together to amortize the I/O cost. We developed a serial version; a parallel version for shared-memory and shared-disk multi-core systems; and a parallel version for shared-nothing architectures. ERa indexes the entire human genome in 19 minutes on an ordinary desktop computer. For comparison, the fastest existing method needs 15 minutes using 1024 CPUs on an IBM BlueGene supercomputer.


Fast Updates on Read-Optimized Databases Using Multi-Core CPUs

Abstract: Read-optimized columnar databases use differential updates to handle writes by maintaining a separate write-optimized delta partition which is periodically merged with the read-optimized and compressed main partition. This merge process introduces significant overheads and unacceptable downtimes in update intensive systems, aspiring to combine transactional and analytical workloads into one system. In the first part of the paper, we report data analyses of 12 SAP Business Suite customer systems. In the second half, we present an optimized merge process reducing the merge overhead of current systems by a factor of 30. Our linear-time merge algorithm exploits the underlying high compute and bandwidth resources of modern multi-core CPUs with architecture-aware optimizations and efficient parallelization. This enables compressed in-memory column stores to handle the transactional update rate required by enterprise applications, while keeping properties of read-optimized databases for analytic-style queries.


A Data-Based Approach to Social Influence Maximization

Abstract: Influence maximization is the problem of finding a set of users in a social network, such that by targeting this set, one maximizes the expected spread of influence in the network. Most of the literature on this topic has focused exclusively on the social graph, overlooking historical data, i.e., traces of past action propagations. In this paper, we study influence maximization from a novel data-based perspective. In particular, we introduce a new model, which we call credit distribution, that directly leverages available propagation traces to learn how influence flows in the network and uses this to estimate expected influence spread. Our approach also learns the different levels of influenceability of users, and it is time-aware in the sense that it takes the temporal nature of influence into account. We show that influence maximization under the credit distribution model is NP-hard and that the function that defines expected spread under our model is submodular. Based on these, we develop an approximation algorithm for solving the influence maximization problem that at once enjoys high accuracy compared to the standard approach, while being several orders of magnitude faster and more scalable.


A Semantic Hierarchy for Erasure Policies

Abstract: We consider the problem of logical data erasure, contrasting with physical erasure in the same way that end-to-end information flow control contrasts with access control. We present a semantic hierarchy for erasure policies, using a possibilistic knowledge-based semantics to define policy satisfaction such that there is an intuitively clear upper bound on what information an erasure policy permits to be retained. Our hierarchy allows a rich class of erasure policies to be expressed, taking account of the power of the attacker, how much information may be retained, and under what conditions it may be retained. While our main aim is to specify erasure policies, the semantic framework allows quite general information-flow policies to be formulated for a variety of semantic notions of secrecy.


Distributed Selfish Load Balancing with Weights and Speeds

Abstract: In this paper we consider neighborhood load balancing in the context of selfish clients. We assume that a network of n processors and m tasks is given. The processors may have different speeds and the tasks may have different weights. Every task is controlled by a selfish user. The objective of the user is to allocate his/her task to a processor with minimum load. We revisit the concurrent probabilistic protocol introduced in [6], which works in sequential rounds. In each round every task is allowed to query the load of one randomly chosen neighboring processor. If that load is smaller the task will migrate to that processor with a suitably chosen probability. Using techniques from spectral graph theory we obtain upper bounds on the expected convergence time towards approximate and exact Nash equilibria that are significantly better than the previous results in [6]. We show results for uniform tasks on non-uniform processors and the general case where the tasks have different weights and the machines have speeds. To the best of our knowledge, these are the first results for this general setting.


Conditional Model Checking

Abstract: Software model checking, as an undecidable problem, has three possible outcomes: (1) the program satisfies the specification, (2) the program does not satisfy the specification, and (3) the model checker fails. The third outcome usually manifests itself in a space-out, time-out, or one component of the verification tool giving up; in all of these failing cases, significant computation is performed by the verification tool before the failure, but no result is reported. We propose to reformulate the model-checking problem as follows, in order to have the verification tool report a summary of the performed work even in case of failure: given a program and a specification, the model checker returns a condition P ---usually a state predicate--- such that the program satisfies the specification under the condition P ---that is, as long as the program does not leave states in which P is satisfied. We are of course interested in model checkers that return conditions P that are as weak as possible. Instead of outcome (1), the model checker will return P = true; instead of (2), the condition P will return the part of the state space that satisfies the specification; and in case (3), the condition P can summarize the work that has been performed by the model checker before space-out, time-out, or giving up. If complete verification is necessary, then a different verification method or tool may be used to focus on the states that violate the condition. We give such conditions as input to a conditional model checker, such that the verification problem is restricted to the part of the state space that satisfies the condition. Our experiments show that repeated application of conditional model checkers, using different conditions, can significantly improve the verification results, state-space coverage, and performance.


Causes of Ineradicable Spurious Predictions in Qualitative Simulation

Abstract: It was recently proved that a sound and complete qualitative simulator does not exist, that is, as long as the input-output vocabulary of the state-of-the-art QSIM algorithm is used, there will always be input models which cause any simulator with a coverage guarantee to make spurious predictions in its output. In this paper, we examine whether a meaningfully expressive restriction of this vocabulary is possible so that one can build a simulator with both the soundness and completeness properties. We prove several negative results: All sound qualitative simulators, employing subsets of the QSIM representation which retain the operating region transition feature, and support at least the addition and constancy constraints, are shown to be inherently incomplete. Even when the simulations are restricted to run in a single operating region, a constraint vocabulary containing just the addition, constancy, derivative, and multiplication relations makes the construction of sound and complete qualitative simulators impossible.


Feature-Aware Verification

Abstract: A software product line is a set of software products that are distinguished in terms of features (i.e., end-user--visible units of behavior). Feature interactions ---situations in which the combination of features leads to emergent and possibly critical behavior--- are a major source of failures in software product lines. We explore how feature-aware verification can improve the automatic detection of feature interactions in software product lines. Feature-aware verification uses product-line verification techniques and supports the specification of feature properties along with the features in separate and composable units. It integrates the technique of variability encoding to verify a product line without generating and checking a possibly exponential number of feature combinations. We developed the tool suite SPLverifier for feature-aware verification, which is based on standard model-checking technology. We applied it to an e-mail system that incorporates domain knowledge of AT&T. We found that feature interactions can be detected automatically based on specifications that have only feature-local knowledge, and that variability encoding significantly improves the verification performance when proving the absence of interactions.


Properties and Applications of Programs with Monotone and Convex Constraints

Abstract: We study properties of programs with monotone and convex constraints. We extend to these formalisms concepts and results from normal logic programming. They include the notions of strong and uniform equivalence with their characterizations, tight programs and Fages Lemma, program completion and loop formulas. Our results provide an abstract account of properties of some recent extensions of logic programming with aggregates, especially the formalism of lparse programs. They imply a method to compute stable models of lparse programs by means of off-the-shelf solvers of pseudo-boolean constraints, which is often much faster than the smodels system.


How the Landscape of Random Job Shop Scheduling Instances Depends on the Ratio of Jobs to Machines

Abstract: We characterize the search landscape of random instances of the job shop scheduling problem (JSP). Specifically, we investigate how the expected values of (1) backbone size, (2) distance between near-optimal schedules, and (3) makespan of random schedules vary as a function of the job to machine ratio (N/M). For the limiting cases N/M approaches 0 and N/M approaches infinity we provide analytical results, while for intermediate values of N/M we perform experiments. We prove that as N/M approaches 0, backbone size approaches 100%, while as N/M approaches infinity the backbone vanishes. In the process we show that as N/M approaches 0 (resp. N/M approaches infinity), simple priority rules almost surely generate an optimal schedule, providing theoretical evidence of an "easy-hard-easy" pattern of typical-case instance difficulty in job shop scheduling. We also draw connections between our theoretical results and the "big valley" picture of JSP landscapes.


Computationally Feasible VCG Mechanisms

Abstract: A major achievement of mechanism design theory is a general method for the construction of truthful mechanisms called VCG (Vickrey, Clarke, Groves). When applying this method to complex problems such as combinatorial auctions, a difficulty arises: VCG mechanisms are required to compute optimal outcomes and are, therefore, computationally infeasible. However, if the optimal outcome is replaced by the results of a sub-optimal algorithm, the resulting mechanism (termed VCG-based) is no longer necessarily truthful. The first part of this paper studies this phenomenon in depth and shows that it is near universal. Specifically, we prove that essentially all reasonable approximations or heuristics for combinatorial auctions as well as a wide class of cost minimization problems yield non-truthful VCG-based mechanisms. We generalize these results for affine maximizers. The second part of this paper proposes a general method for circumventing the above problem. We introduce a modification of VCG-based mechanisms in which the agents are given a chance to improve the output of the underlying algorithm. When the agents behave truthfully, the welfare obtained by the mechanism is at least as good as the one obtained by the algorithms output. We provide a strong rationale for truth-telling behavior. Our method satisfies individual rationality as well.


Preference-based Search using Example-Critiquing with Suggestions

Abstract: We consider interactive tools that help users search for their most preferred item in a large collection of options. In particular, we examine example-critiquing, a technique for enabling users to incrementally construct preference models by critiquing example options that are presented to them. We present novel techniques for improving the example-critiquing technology by adding suggestions to its displayed options. Such suggestions are calculated based on an analysis of users current preference model and their potential hidden preferences. We evaluate the performance of our model-based suggestion techniques with both synthetic and real users. Results show that such suggestions are highly attractive to users and can stimulate them to express more preferences to improve the chance of identifying their most preferred item by up to 78%.


Anytime Point-Based Approximations for Large POMDPs

Abstract: The Partially Observable Markov Decision Process has long been recognized as a rich framework for real-world planning and control problems, especially in robotics. However exact solutions in this framework are typically computationally intractable for all but the smallest problems. A well-known technique for speeding up POMDP solving involves performing value backups at specific belief points, rather than over the entire belief simplex. The efficiency of this approach, however, depends greatly on the selection of points. This paper presents a set of novel techniques for selecting informative belief points which work well in practice. The point selection procedure is combined with point-based value backups to form an effective anytime POMDP algorithm called Point-Based Value Iteration (PBVI). The first aim of this paper is to introduce this algorithm and present a theoretical analysis justifying the choice of belief selection technique. The second aim of this paper is to provide a thorough empirical comparison between PBVI and other state-of-the-art POMDP methods, in particular the Perseus algorithm, in an effort to highlight their similarities and differences. Evaluation is performed using both standard POMDP domains and realistic robotic tasks.


Solving Factored MDPs with Hybrid State and Action Variables

Abstract: Efficient representations and solutions for large decision problems with continuous and discrete variables are among the most important challenges faced by the designers of automated decision support systems. In this paper, we describe a novel hybrid factored Markov decision process (MDP) model that allows for a compact representation of these problems, and a new hybrid approximate linear programming (HALP) framework that permits their efficient solutions. The central idea of HALP is to approximate the optimal value function by a linear combination of basis functions and optimize its weights by linear programming. We analyze both theoretical and computational aspects of this approach, and demonstrate its scale-up potential on several hybrid optimization problems.


Combination Strategies for Semantic Role Labeling

Abstract: This paper introduces and analyzes a battery of inference models for the problem of semantic role labeling: one based on constraint satisfaction, and several strategies that model the inference as a meta-learning problem using discriminative classifiers. These classifiers are developed with a rich set of novel features that encode proposition and sentence-level information. To our knowledge, this is the first work that: (a) performs a thorough analysis of learning-based inference models for semantic role labeling, and (b) compares several inference strategies in this context. We evaluate the proposed inference strategies in the framework of the CoNLL-2005 shared task using only automatically-generated syntactic information. The extensive experimental evaluation and analysis indicates that all the proposed inference strategies are successful -they all outperform the current best results reported in the CoNLL-2005 evaluation exercise- but each of the proposed approaches has its advantages and disadvantages. Several important traits of a state-of-the-art SRL combination strategy emerge from this analysis: (i) individual models should be combined at the granularity of candidate arguments rather than at the granularity of complete solutions; (ii) the best combination strategy uses an inference model based in learning; and (iii) the learning-based inference benefits from max-margin classifiers and global feedback.


Learning image transformations without training examples

Abstract: The use of image transformations is essential for efficient modeling and learning of visual data. But the class of relevant transformations is large: affine transformations, projective transformations, elastic deformations, ... the list goes on. Therefore, learning these transformations, rather than hand coding them, is of great conceptual interest. To the best of our knowledge, all the related work so far has been concerned with either supervised or weakly supervised learning (from correlated sequences, video streams, or image-transform pairs). In this paper, on the contrary, we present a simple method for learning affine and elastic transformations when no examples of these transformations are explicitly given, and no prior knowledge of space (such as ordering of pixels) is included either. The system has only access to a moderately large database of natural images arranged in no particular order.


Enhance accuracy in Software cost and schedule estimation by using "Uncertainty Analysis and Assessment" in the system modeling process

Abstract: Accurate software cost and schedule estimation are essential for software project success. Often it referred to as the "black art" because of its complexity and uncertainty, software estimation is not as difficult or puzzling as people think. In fact, generating accurate estimates is straightforward-once you understand the intensity of uncertainty and framework for the modeling process. The mystery to successful software estimation-distilling academic information and real-world experience into a practical guide for working software professionals. Instead of arcane treatises and rigid modeling techniques, this will guide highlights a proven set of procedures, understandable formulas, and heuristics that individuals and development teams can apply to their projects to help achieve estimation proficiency with choose appropriate development approaches In the early stage of software life cycle project manager are inefficient to estimate the effort, schedule, cost estimation and its development approach .This in turn, confuses the manager to bid effectively on software project and choose incorrect development approach. That will directly effect on productivity cycle and increase level of uncertainty. This becomes a strong cause of project failure. So to avoid such problem if we know level and sources of uncertainty in model design, It will directive the developer to design accurate software cost and schedule estimation, which are essential for software project success. However once the required efforts have estimated, little is done to recalibrate and reduce the uncertainty of the initial estimates.


Multi-Agent Programming Contest 2011 - The Python-DTU Team

Abstract: We provide a brief description of the Python-DTU system, including the overall design, the tools and the algorithms that we plan to use in the agent contest.


CSI-aided MAC with Multiuser Diversity for Cognitive Radio Networks

Abstract: Cognitive Radio (CR) aims to increase the spectrum utilization by allowing secondary users (SU) to access unused licensed spectrum bands. To maximize the throughput given limited sensing capability, SUs need to strike a balance between sensing the channels that are not heavily used by primary users (PU) and avoiding collisions with other SUs. To randomize sensing decisions without resorting to multiuser sensing policies, it is proposed to exploit the spatially-variant fading channel conditions on different links by adapting the reward to the channel state information (CSI). Moreover, the proposed channel-adaptive policy favors links with high achievable transmission rate and thus further improves the network throughput.


Reputation- and Trust-Based Systems for Wireless Self-organizing Networks

Abstract: Traditional approach of providing network security has been to borrow tools and mechanisms from cryptography. However, the conventional view of security based on cryptography alone is not sufficient for the defending against unique and novel types of misbehavior exhibited by nodes in wireless self-organizing networks such as mobile ad hoc networks and wireless sensor networks. Reputation-based frameworks, where nodes maintain reputation of other nodes and use it to evaluate their trustworthiness, are deployed to provide scalable, diverse and a generalized approach for countering different types of misbehavior resulting form malicious and selfish nodes in these networks. In this chapter, we present a comprehensive discussion on reputation and trust-based systems for wireless self-organizing networks. Different classes of reputation system are described along with their unique characteristics and working principles. A number of currently used reputation systems are critically reviewed and compared with respect to their effectiveness and efficiency of performance. Some open problems in the area of reputation and trust-based system within the domain of wireless self-organizing networks are also discussed.


Cross-Layer Protocols for Multimedia Communications over Wireless Networks

Abstract: In the last few years, the Internet throughput, usage and reliability have increased almost exponentially. The introduction of broadband wireless mobile ad hoc networks (MANETs) and cellular networks together with increased computational power have opened the door for a new breed of applications to be created, namely real-time multimedia applications. Delivering real-time multimedia traffic over a complex network like the Internet is a particularly challenging task since these applications have strict quality-of-service (QoS) requirements on bandwidth, delay, and delay jitter. Traditional Internet protocol (IP)-based best effort service is not able to meet these stringent requirements. The time-varying nature of wireless channels and resource constrained wireless devices make the problem even more difficult. To improve perceived media quality by end users over wireless Internet, QoS supports can be addressed in different layers, including application layer, transport layer and link layer. Cross layer design is a well-known approach to achieve this adaptation. In cross-layer design, the challenges from the physical wireless medium and the QoS-demands from the applications are taken into account so that the rate, power, and coding at the physical (PHY) layer can adapted to meet the requirements of the applications given the current channel and network conditions. A number of propositions for cross-layer designs exist in the literature. In this chapter, an extensive review has been made on these cross-layer architectures that combine the application-layer, transport layer and the link layer controls. Particularly, the issues like channel estimation techniques, adaptive controls at the application and link layers for energy efficiency, priority based scheduling, transmission rate control at the transport layer, and adaptive automatic repeat request (ARQ) are discussed in detail.


Secure and Privacy- Aware Searching in Peer-to-Peer Networks

Abstract: The existing peer-to-peer networks have several problems such as fake content distribution, free riding, white-washing and poor search scalability, lack of a robust trust model and absence of user privacy protection mechanism. Although, several trust management and semantic community-based mechanisms for combating free riding and distribution of malicious contents have been proposed by some researchers, most of these schemes lack scalability due to their high computational, communication and storage overhead. This paper presents a robust trust management scheme for P2P networks that utilizes topology adaptation by constructing an overlay of trusted peers where the neighbors are selected based on their trust ratings and content similarities. While increasing the search efficiency by intelligently exploiting the formation of semantic community structures by topology adaptation among the trustworthy peers, the scheme provides the users a very high level of privacy protection of their usage and consumption patterns of network resources. Simulation results demonstrate that the proposed scheme provides efficient searching to good peers while penalizing the malicious peers by increasing their search times as the network topology stabilizes.


An efficient algorithm to find a set of nearest elements in a mesh

Abstract: A linear time algorithm to find a set of nearest elements in a mesh.


Parameterized complexity in multiple-interval graphs: domination, partition, separation, irredundancy

Abstract: We show that the problem k-Dominating Set and its several variants including k-Connected Dominating Set, k-Independent Dominating Set, and k-Dominating Clique, when parameterized by the solution size k, are W[1]-hard in either multiple-interval graphs or their complements or both. On the other hand, we show that these problems belong to W[1] when restricted to multiple-interval graphs and their complements. This answers an open question of Fellows et al. In sharp contrast, we show that d-Distance k-Dominating Set for d >= 2 is W[2]-complete in multiple-interval graphs and their complements. We also show that k-Perfect Code and d-Distance k-Perfect Code for d >= 2 are W[1]-complete even in unit 2-track interval graphs. In addition, we present various new results on the parameterized complexities of k-Vertex Clique Partition and k-Separating Vertices in multiple-interval graphs and their complements, and present a very simple alternative proof of the W[1]-hardness of k-Irredundant Set in general graphs.


Self-Adjusting Networks to Minimize Expected Path Length

Abstract: Given a network infrastructure (e.g., data-center or on-chip-network) and a distribution on the source-destination requests, the expected path (route) length is an important measure for the performance, efficiency and power consumption of the network. In this work we initiate a study on self-adjusting networks: networks that use local-distributed mechanisms to adjust the position of the nodes (e.g., virtual machines) in the network to best fit the route requests distribution. Finding the optimal placement of nodes is defined as the minimum expected path length (MEPL) problem. This is a generalization of the minimum linear arrangement (MLA) problem where the network infrastructure is a line and the computation is done centrally. In contrast to previous work, we study the distributed version and give efficient and simple approximation algorithms for interesting and practically relevant special cases of the problem. In particular, we consider grid networks in which the distribution of requests is a symmetric product distribution. In this setting, we show that a simple greedy policy of position switching between neighboring nodes to locally minimize an objective function, achieves good approximation ratios. We are able to prove this result using the useful notions of expected rank of the distribution and the expected distance to the center of the graph.


NP is not AL and P is not NC is not NL is not L

Abstract: This paper talk about that NP is not AL and P, P is not NC, NC is not NL, and NL is not L. The point about this paper is the depend relation of the problem that need other problem's result to compute it. I show the structure of depend relation that could divide each complexity classes.


A note on Pr\"ufer-like coding and counting forests of uniform hypertrees

Abstract: This note presents an encoding and a decoding algorithms for a forest of (labelled) rooted uniform hypertrees and hypercycles in linear time, by using as few as $n - 2$ integers in the range $[1,n]$. It is a simple extension of the classical Pr\"{u}fer code for (labelled) rooted trees to an encoding for forests of (labelled) rooted uniform hypertrees and hypercycles, which allows to count them up according to their number of vertices, hyperedges and hypertrees. In passing, we also find Cayley's formula for the number of (labelled) rooted trees as well as its generalisation to the number of hypercycles found by Selivanov in the early 70's.


Analysing complexity of XML Schemas in geospatial web services

Abstract: XML Schema is the language used to define the structure of messages exchanged between OGC-based web service clients and providers. The size of these schemas has been growing with time, reaching a state that makes its understanding and effective application a hard task. A first step to cope with this situation is to provide different ways to measure the complexity of the schemas. In this regard, we present in this paper an analysis of the complexity of XML schemas in OGC web services. We use a group of metrics found in the literature and introduce new metrics to measure size and/or complexity of these schemas. The use of adequate metrics allows us to quantify the complexity, quality and other properties of the schemas, which can be very useful in different scenarios.


Dealing with large schema sets in mobile SOS-based applications

Abstract: Although the adoption of OGC Web Services for server, desktop and web applications has been successful, its penetration in mobile devices has been slow. One of the main reasons is the performance problems associated with XML processing as it consumes a lot of memory and processing time, which are scarce resources in a mobile device. In this paper we propose an algorithm to generate efficient code for XML data binding for mobile SOS-based applications. The algorithm take advantage of the fact that individual implementations use only some portions of the standards' schemas, which allows the simplification of large XML schema sets in an application-specific manner by using a subset of XML instance files conforming to these schemas.


Eclectic Extraction of Propositional Rules from Neural Networks

Abstract: Artificial Neural Network is among the most popular algorithm for supervised learning. However, Neural Networks have a well-known drawback of being a "Black Box" learner that is not comprehensible to the Users. This lack of transparency makes it unsuitable for many high risk tasks such as medical diagnosis that requires a rational justification for making a decision. Rule Extraction methods attempt to curb this limitation by extracting comprehensible rules from a trained Network. Many such extraction algorithms have been developed over the years with their respective strengths and weaknesses. They have been broadly categorized into three types based on their approach to use internal model of the Network. Eclectic Methods are hybrid algorithms that combine the other approaches to attain more performance. In this paper, we present an Eclectic method called HERETIC. Our algorithm uses Inductive Decision Tree learning combined with information of the neural network structure for extracting logical rules. Experiments and theoretical analysis show HERETIC to be better in terms of speed and performance.


A Behavioral Distance for Fuzzy-Transition Systems

Abstract: In contrast to the existing approaches to bisimulation for fuzzy systems, we introduce a behavioral distance to measure the behavioral similarity of states in a nondeterministic fuzzy-transition system. This behavioral distance is defined as the greatest fixed point of a suitable monotonic function and provides a quantitative analogue of bisimilarity. The behavioral distance has the important property that two states are at zero distance if and only if they are bisimilar. Moreover, for any given threshold, we find that states with behavioral distances bounded by the threshold are equivalent. In addition, we show that two system combinators---parallel composition and product---are non-expansive with respect to our behavioral distance, which makes compositional verification possible.


Fixed-Parameter Tractability of Directed Multiway Cut Parameterized by the Size of the Cutset

Abstract: Given a directed graph $G$, a set of $k$ terminals and an integer $p$, the \textsc{Directed Vertex Multiway Cut} problem asks if there is a set $S$ of at most $p$ (nonterminal) vertices whose removal disconnects each terminal from all other terminals. \textsc{Directed Edge Multiway Cut} is the analogous problem where $S$ is a set of at most $p$ edges. These two problems indeed are known to be equivalent. A natural generalization of the multiway cut is the \emph{multicut} problem, in which we want to disconnect only a set of $k$ given pairs instead of all pairs. Marx (Theor. Comp. Sci. 2006) showed that in undirected graphs multiway cut is fixed-parameter tractable (FPT) parameterized by $p$. Marx and Razgon (STOC 2011) showed that undirected multicut is FPT and directed multicut is W[1]-hard parameterized by $p$. We complete the picture here by our main result which is that both \textsc{Directed Vertex Multiway Cut} and \textsc{Directed Edge Multiway Cut} can be solved in time $2^{2^{O(p)}}n^{O(1)}$, i.e., FPT parameterized by size $p$ of the cutset of the solution. This answers an open question raised by Marx (Theor. Comp. Sci. 2006) and Marx and Razgon (STOC 2011). It follows from our result that \textsc{Directed Multicut} is FPT for the case of $k=2$ terminal pairs, which answers another open problem raised in Marx and Razgon (STOC 2011).


Face Recognition using Optimal Representation Ensemble

Abstract: Recently, the face recognizers based on linear representations have been shown to deliver state-of-the-art performance. In real-world applications, however, face images usually suffer from expressions, disguises and random occlusions. The problematic facial parts undermine the validity of the linear-subspace assumption and thus the recognition performance deteriorates significantly. In this work, we address the problem in a learning-inference-mixed fashion. By observing that the linear-subspace assumption is more reliable on certain face patches rather than on the holistic face, some Bayesian Patch Representations (BPRs) are randomly generated and interpreted according to the Bayes' theory. We then train an ensemble model over the patch-representations by minimizing the empirical risk w.r.t the "leave-one-out margins". The obtained model is termed Optimal Representation Ensemble (ORE), since it guarantees the optimality from the perspective of Empirical Risk Minimization. To handle the unknown patterns in test faces, a robust version of BPR is proposed by taking the non-face category into consideration. Equipped with the Robust-BPRs, the inference ability of ORE is increased dramatically and several record-breaking accuracies (99.9% on Yale-B and 99.5% on AR) and desirable efficiencies (below 20 ms per face in Matlab) are achieved. It also overwhelms other modular heuristics on the faces with random occlusions, extreme expressions and disguises. Furthermore, to accommodate immense BPRs sets, a boosting-like algorithm is also derived. The boosted model, a.k.a Boosted-ORE, obtains similar performance to its prototype. Besides the empirical superiorities, two desirable features of the proposed methods, namely, the training-determined model-selection and the data-weight-free boosting procedure, are also theoretically verified.


Repr\'esentation de donn\'ees et m\'etadonn\'ees dans une biblioth\`eque virtuelle pour une ad\'equation avec l'usager et les outils de glanage ou moissonnage scientifique

Abstract: The vehicles for official knowledge, as well as university libraries, suffer from an increasingly visible lack of interest. This is due to the advent of fully digital practices. By studying the psychological and cognitive models in information retrieval initiated in the 1980s, it is possible to use these theories and apply them practically to the Information Retrieval System, taking into account the requirements of virtual libraries. New metadata standards along with modern tools that help managing references should help automating the process of scientific research. We offer a practical implementation of the given theories to test them when they are applied to the information retrieval in computer sciences. This case under study will highlight good practices in gleaning and harvesting scientific literature.


A Taxonomy of Daemons in Self-stabilization

Abstract: We survey existing scheduling hypotheses made in the literature in self-stabilization, commonly referred to under the notion of daemon. We show that four main characteristics (distribution, fairness, boundedness, and enabledness) are enough to encapsulate the various differences presented in existing work. Our naming scheme makes it easy to compare daemons of particular classes, and to extend existing possibility or impossibility results to new daemons. We further examine existing daemon transformer schemes and provide the exact transformed characteristics of those transformers in our taxonomy.


OntologyNavigator: WEB 2.0 scalable ontology based CLIR portal to IT scientific corpus for researchers

Abstract: This work presents the architecture used in the ongoing OntologyNavigator project. It is a research tool to help advanced learners to find adapted IT papers to create scientific bibliographies. The purpose is the use of an IT representation as educational research software for researchers. We use an ontology based on the ACM's Computing Classification System in order to find scientific papers directly related to the new researcher's domain without any formal request. An ontology translation in French is automatically proposed and can be based on Web 2.0 enhanced by a community of users. A visualization and navigation model is proposed to make it more accessible and examples are given to show the interface of the tool. This model offers the possibility of cross language query. Users deeply interact with the translation by providing alternative translation of the node label. Customers also enrich the ontology node labels with implicit descriptors.


The firefighter problem with more than one firefighter on trees

Abstract: In this paper we study the complexity of the firefighter problem and related problems on trees when more than one firefighter is available at each time step, and answer several open questions of Finbow and MacGillivray 2009. More precisely, when $b \geq 2$ firefighters are allowed at each time step, the problem is NP-complete for trees of maximum degree $b+2$ and polynomial-time solvable for trees of maximum degree $b+2$ when the fire breaks out at a vertex of degree at most $b+1$. Moreover we present a polynomial-time algorithm for a subclass of trees, namely $k$-caterpillars.


Implementing a Web Browser with Phishing Detection Techniques

Abstract: Phishing is the combination of social engineering and technical exploits designed to convince a victim to provide personal information, usually for the monetary gain of the attacker. Phishing has become the most popular practice among the criminals of the Web. Phishing attacks are becoming more frequent and sophisticated. The impact of phishing is drastic and significant since it can involve the risk of identity theft and financial losses. Phishing scams have become a problem for online banking and e-commerce users. In this paper we propose a novel approach to detect phishing attacks. We implemented a prototype web browser which can be used as an agent and processes each arriving email for phishing attacks. Using email data collected over a period time we demonstrate data that our approach is able to detect more phishing attacks than existing schemes.


Distributed Maximal Matching: Greedy is Optimal

Abstract: We study distributed algorithms that find a maximal matching in an anonymous, edge-coloured graph. If the edges are properly coloured with $k$ colours, there is a trivial greedy algorithm that finds a maximal matching in $k-1$ synchronous communication rounds. The present work shows that the greedy algorithm is optimal in the general case: any algorithm that finds a maximal matching in anonymous, $k$-edge-coloured graphs requires $k-1$ rounds. If we focus on graphs of maximum degree $\Delta$, it is known that a maximal matching can be found in $O(\Delta + \log^* k)$ rounds, and prior work implies a lower bound of $\Omega(\polylog(\Delta) + \log^* k)$ rounds. Our work closes the gap between upper and lower bounds: the complexity is $\Theta(\Delta + \log^* k)$ rounds. To our knowledge, this is the first linear-in-$\Delta$ lower bound for the distributed complexity of a classical graph problem.


Many-Task Computing Tools for Multiscale Modeling

Abstract: This paper discusses the use of many-task computing tools for multiscale modeling. It defines multiscale modeling and places different examples of it on a coupling spectrum, discusses the Swift parallel scripting language, describes three multiscale modeling applications that could use Swift, and then talks about how the Swift model is being extended to cover more of the multiscale modeling coupling spectrum.


The BlueNetwork Concept

Abstract: Most of energy efficiency and carbon reduction initiatives and concepts attempt to regulate and optimize machines behavior, and therefore, human behavior itself is left neglected. Although most of energy and resource consumption is the result of machines functioning and behavior (including domesticated animals such as cows), these behaviors themselves are actually in answer to humans demands and needs, and therefore, can be considered the indirect results of humans behavior. Resolving the source of problems, i.e., the unhealthy human behavior, not only reduces these footprints including energy and water consumption, and GHG emissions, it also helps increasing the quality of life in society. Here, we propose an approach which focuses on adjusting humans behavior toward eliminating unnecessary demand on the machines that consequently lowers the consumption. This goal is achieved by creating a social environment in which directed and selective interactions help humans to adjust to healthier behavior. The solution consists of human-friendly interfaces and also artificial intelligence software in order to learn and emulate human interactions.


LSM is not generated by binary functions

Abstract: The material in this note is now superseded by arXiv:1108.5288v4. Bulatov et al. [1] defined the operation of (efficient) pps_\omega-definability in order to study the computational complexity of certain approximate counting problems. They asked whether all log-supermodular functions can be defined by binary implication and unary functions in this sense. We give a negative answer to this question.


A binary noisy channel to model errors in printing process

Abstract: To model printing noise a binary noisy channel and a set of controlled gates are introduced. The channel input is an image created by a halftoning algorithm and its output is the printed picture. Using this channel robustness to noise between halftoning algorithms can be studied. We introduced relative entropy to describe immunity of the algorithm to noise and tested several halftoning algorithms.


Distributed Evolutionary Graph Partitioning

Abstract: We present a novel distributed evolutionary algorithm, KaFFPaE, to solve the Graph Partitioning Problem, which makes use of KaFFPa (Karlsruhe Fast Flow Partitioner). The use of our multilevel graph partitioner KaFFPa provides new effective crossover and mutation operators. By combining these with a scalable communication protocol we obtain a system that is able to improve the best known partitioning results for many inputs in a very short amount of time. For example, in Walshaw's well known benchmark tables we are able to improve or recompute 76% of entries for the tables with 1%, 3% and 5% imbalance.


Distance Preserving Graph Simplification

Abstract: Large graphs are difficult to represent, visualize, and understand. In this paper, we introduce "gate graph" - a new approach to perform graph simplification. A gate graph provides a simplified topological view of the original graph. Specifically, we construct a gate graph from a large graph so that for any "non-local" vertex pair (distance higher than some threshold) in the original graph, their shortest-path distance can be recovered by consecutive "local" walks through the gate vertices in the gate graph. We perform a theoretical investigation on the gate-vertex set discovery problem. We characterize its computational complexity and reveal the upper bound of minimum gate-vertex set using VC-dimension theory. We propose an efficient mining algorithm to discover a gate-vertex set with guaranteed logarithmic bound. We further present a fast technique for pruning redundant edges in a gate graph. The detailed experimental results using both real and synthetic graphs demonstrate the effectiveness and efficiency of our approach.


Boolean Satisfiability using Noise Based Logic

Abstract: In this paper, we present a novel algorithm to solve the Boolean Satisfiability (SAT) problem, using noise-based logic (NBL). Contrary to what the name may suggest, NBL is not a random/fuzzy logic system. In fact, it is a completely deterministic logic system. A key property of NBL is that it allows us to apply a superposition of many input vectors to a SAT instance at the same time, circumventing a key restriction and assumption in the traditional approach to solving SAT. By exploiting the superposition property of NBL, our NBL-based SAT algorithm can determine whether an instance is SAT or not in a single operation. A satisfying solution can be found by iteratively performing SAT check operations up to n times, where n is the number of variables in the SAT instance. Although this paper does not focus on the realization of an NBL-based SAT engine, such an engine can be conceived using analog circuits (wide-band amplifiers, adders and multipliers), FPGAs or ASICs. Additionally, we also discus scalability of our approach, which can apply to NBL in general. The NBL-based SAT engine described in this paper has been simulated in software for validation purposes.


Open Input: A New Way for Websites to Grow

Abstract: Regardless of current web 2.0 and 3.0 trends, there are still a lot of websites made in web 1.0 style. These websites have fixed pages which are editable only by owner and not by community. It is normal for a lot of cases, but looks like not modern and engaging approach. Are there any ways to make these sites closer to life? This paper is devoted to open input technique, a way for websites of web 1.0 era to grow and evolve community. The idea of open input, in general, means that anybody from the web can add information to any section of the website even without registration on that website. People can add news, billboard announcements, testimonials, questions, pictures, videos etc - whatever site owner permitted. We have tested this idea in practice and have positive results approving that open input is a vital approach for collaboration on the web.


Algorithms for the strong chromatic index of Halin graphs, distance-hereditary graphs and maximal outerplanar graphs

Abstract: We show that there exist linear-time algorithms that compute the strong chromatic index of Halin graphs, of maximal outerplanar graphs and of distance-hereditary graphs.


Discriminately Decreasing Discriminability with Learned Image Filters

Abstract: In machine learning and computer vision, input images are often filtered to increase data discriminability. In some situations, however, one may wish to purposely decrease discriminability of one classification task (a "distractor" task), while simultaneously preserving information relevant to another (the task-of-interest): For example, it may be important to mask the identity of persons contained in face images before submitting them to a crowdsourcing site (e.g., Mechanical Turk) when labeling them for certain facial attributes. Another example is inter-dataset generalization: when training on a dataset with a particular covariance structure among multiple attributes, it may be useful to suppress one attribute while preserving another so that a trained classifier does not learn spurious correlations between attributes. In this paper we present an algorithm that finds optimal filters to give high discriminability to one task while simultaneously giving low discriminability to a distractor task. We present results showing the effectiveness of the proposed technique on both simulated data and natural face images.


Two Projection Pursuit Algorithms for Machine Learning under Non-Stationarity

Abstract: This thesis derives, tests and applies two linear projection algorithms for machine learning under non-stationarity. The first finds a direction in a linear space upon which a data set is maximally non-stationary. The second aims to robustify two-way classification against non-stationarity. The algorithm is tested on a key application scenario, namely Brain Computer Interfacing.


A 2.75-Approximation Algorithm for the Unconstrained Traveling Tournament Problem

Abstract: A 2.75-approximation algorithm is proposed for the unconstrained traveling tournament problem, which is a variant of the traveling tournament problem. For the unconstrained traveling tournament problem, this is the first proposal of an approximation algorithm with a constant approximation ratio. In addition, the proposed algorithm yields a solution that meets both the no-repeater and mirrored constraints. Computational experiments show that the algorithm generates solutions of good quality.


On the Parameterized Complexity of Default Logic and Autoepistemic Logic

Abstract: We investigate the application of Courcelle's Theorem and the logspace version of Elberfeld etal. in the context of the implication problem for propositional sets of formulae, the extension existence problem for default logic, as well as the expansion existence problem for autoepistemic logic and obtain fixed-parameter time and space efficient algorithms for these problems. On the other hand, we exhibit, for each of the above problems, families of instances of a very simple structure that, for a wide range of different parameterizations, do not have efficient fixed-parameter algorithms (even in the sense of the large class XPnu), unless P=NP.


Autonomous Agents Coordination: Action Languages meet CLP(FD) and Linda

Abstract: The paper presents a knowledge representation formalism, in the form of a high-level Action Description Language for multi-agent systems, where autonomous agents reason and act in a shared environment. Agents are autonomously pursuing individual goals, but are capable of interacting through a shared knowledge repository. In their interactions through shared portions of the world, the agents deal with problems of synchronization and concurrency; the action language allows the description of strategies to ensure a consistent global execution of the agents' autonomously derived plans. A distributed planning problem is formalized by providing the declarative specifications of the portion of the problem pertaining a single agent. Each of these specifications is executable by a stand-alone CLP-based planner. The coordination among agents exploits a Linda infrastructure. The proposal is validated in a prototype implementation developed in SICStus Prolog. To appear in Theory and Practice of Logic Programming (TPLP).


Well-Definedness and Efficient Inference for Probabilistic Logic Programming under the Distribution Semantics

Abstract: The distribution semantics is one of the most prominent approaches for the combination of logic programming and probability theory. Many languages follow this semantics, such as Independent Choice Logic, PRISM, pD, Logic Programs with Annotated Disjunctions (LPADs) and ProbLog. When a program contains functions symbols, the distribution semantics is well-defined only if the set of explanations for a query is finite and so is each explanation. Well-definedness is usually either explicitly imposed or is achieved by severely limiting the class of allowed programs. In this paper we identify a larger class of programs for which the semantics is well-defined together with an efficient procedure for computing the probability of queries. Since LPADs offer the most general syntax, we present our results for them, but our results are applicable to all languages under the distribution semantics. We present the algorithm "Probabilistic Inference with Tabling and Answer subsumption" (PITA) that computes the probability of queries by transforming a probabilistic program into a normal program and then applying SLG resolution with answer subsumption. PITA has been implemented in XSB and tested on six domains: two with function symbols and four without. The execution times are compared with those of ProbLog, cplint and CVE, PITA was almost always able to solve larger problems in a shorter time, on domains with and without function symbols.


Storage Area Network Implementation on an Educational Institute Network Computer Networking and Communication

Abstract: the storage infrastructure is the foundation on which information relies and therefore must support a company's business objectives and business model. In this environment, simply deploying more and faster storage devices is not enough; a new kind of infrastructure is needed, one that provides more enhanced network availability, data accessibility, and system manageability than is provided by today's infrastructure. The SAN meets this challenge. The SAN liberates the storage device, so it is not on a particular server bus, and attaches it directly to the network. In other words, storage is externalized and functionally distributed across the organization. The SAN also enables the centralizing of storage devices and the clustering of servers, which makes for easier and less expensive administration. So the idea is to create an intelligent SAN infrastructure that stretches to meet increased demands, allows highly available and heterogeneous access to expanding information.


The Complexity of Rooted Phylogeny Problems

Abstract: Several computational problems in phylogenetic reconstruction can be formulated as restrictions of the following general problem: given a formula in conjunctive normal form where the literals are rooted triples, is there a rooted binary tree that satisfies the formula? If the formulas do not contain disjunctions, the problem becomes the famous rooted triple consistency problem, which can be solved in polynomial time by an algorithm of Aho, Sagiv, Szymanski, and Ullman. If the clauses in the formulas are restricted to disjunctions of negated triples, Ng, Steel, and Wormald showed that the problem remains NP-complete. We systematically study the computational complexity of the problem for all such restrictions of the clauses in the input formula. For certain restricted disjunctions of triples we present an algorithm that has sub-quadratic running time and is asymptotically as fast as the fastest known algorithm for the rooted triple consistency problem. We also show that any restriction of the general rooted phylogeny problem that does not fall into our tractable class is NP-complete, using known results about the complexity of Boolean constraint satisfaction problems. Finally, we present a pebble game argument that shows that the rooted triple consistency problem (and also all generalizations studied in this paper) cannot be solved by Datalog.


Hierarchical Composable Optimization of Web Pages

Abstract: The process of creating modern Web media experiences is challenged by the need to adapt the content and presentation choices to dynamic real-time fluctuations of user interest across multiple audiences. We introduce FAME - a Framework for Agile Media Experiences - which addresses this scalability problem. FAME allows media creators to define abstract page models that are subsequently transformed into real experiences through algorithmic experimentation. FAME's page models are hierarchically composed of simple building blocks, mirroring the structure of most Web pages. They are resolved into concrete page instances by pluggable algorithms which optimize the pages for specific business goals. Our framework allows retrieving dynamic content from multiple sources, defining the experimentation's degrees of freedom, and constraining the algorithmic choices. It offers an effective separation of concerns in the media creation process, enabling multiple stakeholders with profoundly different skills to apply their crafts and perform their duties independently, composing and reusing each other's work in modular ways.


A Survey of Distributed Data Aggregation Algorithms

Abstract: Distributed data aggregation is an important task, allowing the decentralized determination of meaningful global properties, that can then be used to direct the execution of other applications. The resulting values result from the distributed computation of functions like COUNT, SUM and AVERAGE. Some application examples can found to determine the network size, total storage capacity, average load, majorities and many others. In the last decade, many different approaches have been proposed, with different trade-offs in terms of accuracy, reliability, message and time complexity. Due to the considerable amount and variety of aggregation algorithms, it can be difficult and time consuming to determine which techniques will be more appropriate to use in specific settings, justifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data aggregation algorithms, providing three main contributions. First, it formally defines the concept of aggregation, characterizing the different types of aggregation functions. Second, it succinctly describes the main aggregation techniques, organizing them in a taxonomy. Finally, it provides some guidelines toward the selection and use of the most relevant techniques, summarizing their principal characteristics.


Iterated Hairpin Completions of Non-crossing Words

Abstract: Iterated hairpin completion is an operation on formal languages that is inspired by the hairpin formation in DNA biochemistry. Iterated hairpin completion of a word (or more precisely a singleton language) is always a context-sensitive language and for some words it is known to be non-context-free. However, it is unknown whether regularity of iterated hairpin completion of a given word is decidable. Also the question whether iterated hairpin completion of a word can be context-free but not regular was asked in literature. In this paper we investigate iterated hairpin completions of non-crossing words and, within this setting, we are able to answer both questions. For non-crossing words we prove that the regularity of iterated hairpin completions is decidable and that if iterated hairpin completion of a non-crossing word is not regular, then it is not context-free either.


CinemaGazer: a System for Watching Video at Very High Speed

Abstract: This paper presents a technology that enables the watching of videos at very high speed. Subtitles are widely used in DVD movies, and provide useful supplemental information for understanding video contents. We propose a "two-level fast-forwarding" scheme for videos with subtitles, which controls the speed of playback depending on the context: very fast during segments without language, such as subtitles or speech, and "understandably fast" during segments with such language. This makes it possible to watch videos at a higher speed than usual while preserving the entertainment values of the contents. We also propose "centering" and "fading" features for the display of subtitles to reduce fatigue when watching high-speed video. We implement a versatile video encoder that enables movie viewing with two-level fast-forwarding on any mobile device by specifying the speed of playback, the reading rate, or the overall viewing time. The effectiveness of our proposed method was demonstrated in an evaluation study.


Non-Gaussian Scale Space Filtering with 2 by 2 Matrix of Linear Filters

Abstract: Construction of a scale space with a convolution filter has been studied extensively in the past. It has been proven that the only convolution kernel that satisfies the scale space requirements is a Gaussian type. In this paper, we consider a matrix of convolution filters introduced in [1] as a building kernel for a scale space, and shows that we can construct a non-Gaussian scale space with a $2\times 2$ matrix of filters. The paper derives sufficient conditions for the matrix of filters for being a scale space kernel, and present some numerical demonstrations.


Linearized Additive Classifiers

Abstract: We revisit the additive model learning literature and adapt a penalized spline formulation due to Eilers and Marx, to train additive classifiers efficiently. We also propose two new embeddings based two classes of orthogonal basis with orthogonal derivatives, which can also be used to efficiently learn additive classifiers. This paper follows the popular theme in the current literature where kernel SVMs are learned much more efficiently using a approximate embedding and linear machine. In this paper we show that spline basis are especially well suited for learning additive models because of their sparsity structure and the ease of computing the embedding which enables one to train these models in an online manner, without incurring the memory overhead of precomputing the storing the embeddings. We show interesting connections between B-Spline basis and histogram intersection kernel and show that for a particular choice of regularization and degree of the B-Splines, our proposed learning algorithm closely approximates the histogram intersection kernel SVM. This enables one to learn additive models with almost no memory overhead compared to fast a linear solver, such as LIBLINEAR, while being only 5-6X slower on average. On two large scale image classification datasets, MNIST and Daimler Chrysler pedestrians, the proposed additive classifiers are as accurate as the kernel SVM, while being two orders of magnitude faster to train.