Efficient Multi-Template Learning for Structured Prediction

Abstract: Conditional random field (CRF) and Structural Support Vector Machine (Structural SVM) are two state-of-the-art methods for structured prediction which captures the interdependencies among output variables. The success of these methods is attributed to the fact that their discriminative models are able to account for overlapping features on the whole input observations. These features are usually generated by applying a given set of templates on labeled data, but improper templates may lead to degraded performance. To alleviate this issue, in this paper, we propose a novel multiple template learning paradigm to learn structured prediction and the importance of each template simultaneously, so that hundreds of arbitrary templates could be added into the learning model without caution. This paradigm can be formulated as a special multiple kernel learning problem with exponential number of constraints. Then we introduce an efficient cutting plane algorithm to solve this problem in the primal, and its convergence is presented. We also evaluate the proposed learning paradigm on two widely-studied structured prediction tasks, \emph{i.e.} sequence labeling and dependency parsing. Extensive experimental results show that the proposed method outperforms CRFs and Structural SVMs due to exploiting the importance of each template. Our complexity analysis and empirical results also show that our proposed method is more efficient than OnlineMKL on very sparse and high-dimensional data. We further extend this paradigm for structured prediction using generalized $p$-block norm regularization with $p>1$, and experiments show competitive performances when $p \in [1,2)$.


Multidimensional effective S-adic systems are sofic

Abstract: In this article we prove that multidimensional effective S-adic systems, obtained by applying an effective sequence of substitutions chosen among a finite set of substitutions, are sofic subshifts.


Managing and Querying Web Services Communities: A Survey

Abstract: With the advance of Web Services technologies and the emergence of Web Services into the information space, tremendous opportunities for empowering users and organizations appear in various application domains including electronic commerce, travel, intelligence information gathering and analysis, health care, digital government, etc. However, the technology to organize, search, integrate these Web Services has not kept pace with the rapid growth of the available information space. The number of Web Services to be integrated may be large and continuously changing. To ease and improve the process of Web services discovery in an open environment like the Internet, it is suggested to gather similar Web services into groups known as communities. Although Web services are intensively investigated, the community management issues have not been addressed yet In this paper we draw an overview of several Web services Communities' management approaches based on some currently existing communities platforms and frameworks. We also discuss different approaches for querying and selecting Web services under the umbrella of Web services communities'. We compare the current approaches among each others with respect to some key requirements.


Online Strategies for Intra and Inter Provider Service Migration in Virtual Networks

Abstract: Network virtualization allows one to build dynamic distributed systems in which resources can be dynamically allocated at locations where they are most useful. In order to fully exploit the benefits of this new technology, protocols need to be devised which react efficiently to changes in the demand. This paper argues that the field of online algorithms and competitive analysis provides useful tools to deal with and reason about the uncertainty in the request dynamics, and to design algorithms with provable performance guarantees. As a case study, we describe a system (e.g., a gaming application) where network virtualization is used to support thin client applications for mobile devices to improve their QoS. By decoupling the service from the underlying resource infrastructure, it can be migrated closer to the current client locations while taking into account migration cost. This paper identifies the major cost factors in such a system, and formalizes the corresponding optimization problem. Both randomized and deterministic, gravity center based online algorithms are presented which achieve a good tradeoff between improved QoS and migration cost in the worst-case, both for service migration within an infrastructure provider as well as for networks supporting cross-provider migration. The paper reports on our simulation results and also presents an explicit construction of an optimal offline algorithm which allows, e.g., to evaluate the competitive ratio empirically.


Locating Depots for Capacitated Vehicle Routing

Abstract: We study a location-routing problem in the context of capacitated vehicle routing. The input is a set of demand locations in a metric space and a fleet of k vehicles each of capacity Q. The objective is to locate k depots, one for each vehicle, and compute routes for the vehicles so that all demands are satisfied and the total cost is minimized. Our main result is a constant-factor approximation algorithm for this problem. To achieve this result, we reduce to the k-median-forest problem, which generalizes both k-median and minimum spanning tree, and which might be of independent interest. We give a (3+c)-approximation algorithm for k-median-forest, which leads to a (12+c)-approximation algorithm for the above location-routing problem, for any constant c>0. The algorithm for k-median-forest is just t-swap local search, and we prove that it has locality gap 3+2/t; this generalizes the corresponding result known for k-median. Finally we consider the "non-uniform" k-median-forest problem which has different cost functions for the MST and k-median parts. We show that the locality gap for this problem is unbounded even under multi-swaps, which contrasts with the uniform case. Nevertheless, we obtain a constant-factor approximation algorithm, using an LP based approach.


Near-Optimal Column-Based Matrix Reconstruction

Abstract: We consider low-rank reconstruction of a matrix using its columns and we present asymptotically optimal algorithms for both spectral norm and Frobenius norm reconstruction. The main tools we introduce to obtain our r esults are: (i) the use of fast approximate SVD-like decompositions for column reconstruction, and (ii) two deter ministic algorithms for selecting rows from matrices with orthonormal columns, building upon the sparse represen tation theorem for decompositions of the identity that appeared in \cite{BSS09}.


Teraflop-scale Incremental Machine Learning

Abstract: We propose a long-term memory design for artificial general intelligence based on Solomonoff's incremental machine learning methods. We use R5RS Scheme and its standard library with a few omissions as the reference machine. We introduce a Levin Search variant based on Stochastic Context Free Grammar together with four synergistic update algorithms that use the same grammar as a guiding probability distribution of programs. The update algorithms include adjusting production probabilities, re-using previous solutions, learning programming idioms and discovery of frequent subprograms. Experiments with two training sequences demonstrate that our approach to incremental learning is effective.


A Feature Selection Method for Multivariate Performance Measures

Abstract: Feature selection with specific multivariate performance measures is the key to the success of many applications, such as image retrieval and text classification. The existing feature selection methods are usually designed for classification error. In this paper, we propose a generalized sparse regularizer. Based on the proposed regularizer, we present a unified feature selection framework for general loss functions. In particular, we study the novel feature selection paradigm by optimizing multivariate performance measures. The resultant formulation is a challenging problem for high-dimensional data. Hence, a two-layer cutting plane algorithm is proposed to solve this problem, and the convergence is presented. In addition, we adapt the proposed method to optimize multivariate measures for multiple instance learning problems. The analyses by comparing with the state-of-the-art feature selection methods show that the proposed method is superior to others. Extensive experiments on large-scale and high-dimensional real world datasets show that the proposed method outperforms $l_1$-SVM and SVM-RFE when choosing a small subset of features, and achieves significantly improved performances over SVM$^{perf}$ in terms of $F_1$-score.


Evolutionary Game and Learning for Dynamic Spectrum Access

Abstract: Efficient dynamic spectrum access mechanism is crucial for improving the spectrum utilization. In this paper, we consider the dynamic spectrum access mechanism design with both complete and incomplete network information. When the network information is available, we propose an evolutionary spectrum access mechanism. We use the replicator dynamics to study the dynamics of channel selections, and show that the mechanism achieves an equilibrium that is an evolutionarily stable strategy and is also max-min fair. With incomplete network information, we propose a distributed reinforcement learning mechanism for dynamic spectrum access. Each secondary user applies the maximum likelihood estimation method to estimate its expected payoff based on the local observations, and learns to adjust its mixed strategy for channel selections adaptively over time. We study the convergence of the learning mechanism based on the theory of stochastic approximation, and show that it globally converges to an approximate Nash equilibrium. Numerical results show that the proposed evolutionary spectrum access and distributed reinforcement learning mechanisms achieve up to 82% and 70% performance improvement than a random access mechanism, respectively, and are robust to random perturbations of channel selections.


On the Approximation Performance of Fictitious Play in Finite Games

Abstract: We study the performance of Fictitious Play, when used as a heuristic for finding an approximate Nash equilibrium of a 2-player game. We exhibit a class of 2-player games having payoffs in the range [0,1] that show that Fictitious Play fails to find a solution having an additive approximation guarantee significantly better than 1/2. Our construction shows that for n times n games, in the worst case both players may perpetually have mixed strategies whose payoffs fall short of the best response by an additive quantity 1/2 - O(1/n^(1-delta)) for arbitrarily small delta. We also show an essentially matching upper bound of 1/2 - O(1/n).


Temporal Probabilistic Logic Programs: State and Revision

Abstract: There are numerous applications where we have to deal with temporal uncertainty associated with events. The Temporal Probabilistic (TP) Logic Programs should provide support for valid-time indeterminacy of events, by proposing the concept of an indeterminate instant, that is, an interval of time-points (event's time-window) with an associated, lower and upper, probability distribution. In particular, we propose the new semantics, for the TP Logic Programs of Dekhtyar and Subrahmanian. Our semantics, based on the possible world semantics is a generalization of the possible world semantics for (non temporal) Probabilistic Logic Programming, and we define the new syntax for PT-programs, with time variable explicitly represented in all atoms, and show how the standard role of Herbrand interpretations used as possible worlds for probability distributions is coherently extended to Temporal Probabilistic Logic Programming.


Optimal Strategies in Infinite-state Stochastic Reachability Games

Abstract: We consider perfect-information reachability stochastic games for 2 players on infinite graphs. We identify a subclass of such games, and prove two interesting properties of it: first, Player Max always has optimal strategies in games from this subclass, and second, these games are strongly determined. The subclass is defined by the property that the set of all values can only have one accumulation point -- 0. Our results nicely mirror recent results for finitely-branching games, where, on the contrary, Player Min always has optimal strategies. However, our proof methods are substantially different, because the roles of the players are not symmetric. We also do not restrict the branching of the games. Finally, we apply our results in the context of recently studied One-Counter stochastic games.


A generalization of Hopcroft-Karp algorithm for semi-matchings and covers in bipartite graphs (Maximum semi-matching problem in bipartite graphs)

Abstract: An $(f,g)$-semi-matching in a bipartite graph $G=(U \cup V,E)$ is a set of edges $M \subseteq E$ such that each vertex $u\in U$ is incident with at most $f(u)$ edges of $M$, and each vertex $v\in V$ is incident with at most $g(v)$ edges of $M$. In this paper we give an algorithm that for a graph with $n$ vertices and $m$ edges, $n\le m$, constructs a maximum $(f,g)$-semi-matching in running time $O(m\cdot \min (\sqrt{\sum_{u\in U}f(u)}, \sqrt{\sum_{v\in V}g(v)}))$. Using the reduction of [5], our result on maximum $(f,g)$-semi-matching problem directly implies an algorithm for the optimal semi-matching problem with running time $O(\sqrt{n}m \log n)$.


Fully dynamic maximal matching in O(log n) update time

Abstract: We present an algorithm for maintaining maximal matching in a graph under addition and deletion of edges. Our data structure is randomized that takes O(log n) expected amortized time for each edge update where n is the number of vertices in the graph. While there is a trivial O(n) algorithm for edge update, the previous best known result for this problem for a graph with n vertices and m edges is O({(n+ m)}^{0.7072})which is sub-linear only for a sparse graph. For the related problem of maximum matching, Onak and Rubinfield designed a randomized data structure that achieves O(log^2 n) amortized time for each update for maintaining a c-approximate maximum matching for some large constant c. In contrast, we can maintain a factor two approximate maximum matching in O(log n) expected time per update as a direct corollary of the maximal matching scheme. This in turn also implies a two approximate vertex cover maintenance scheme that takes O(log n) expected time per update.


Designing Flexible GUI to Increase the Acceptance Rate of Product Data Management Systems in Industry

Abstract: Product Data Management (PDM) desktop and web based systems maintain the organizational technical and managerial data to increase the quality of products by improving the processes of development, business process flows, change management, product structure management, project tracking and resource planning. Though PDM is heavily benefiting industry but PDM community is facing a very serious unresolved issue in PDM system development with flexible and user friendly graphical user interface for efficient human machine communication. PDM systems offer different services and functionalities at a time but the graphical user interfaces of most of the PDM systems are not designed in a way that a user (especially a new user) can easily learn and use them. Targeting this issue, a thorough research was conducted in field of Human Computer Interaction; resultant data provides the information about graphical user interface development using rich internet applications. The accomplished goal of this research was to support the field of PDM with a proposition of a conceptual model for the implementation of a flexible web based graphical user interface. The proposed conceptual model was successfully designed into implementation model and a resultant prototype putting values to the field is now available. Describing the proposition in detail the main concept, implementation designs and developed prototype is also discussed in this paper. Moreover in the end, prototype is compared with respective functions of existing PDM systems .i.e., Windchill and CIM to evaluate its effectiveness against targeted challenge


Efficient neuro-fuzzy system and its Memristor Crossbar-based Hardware Implementation

Abstract: In this paper a novel neuro-fuzzy system is proposed where its learning is based on the creation of fuzzy relations by using new implication method without utilizing any exact mathematical techniques. Then, a simple memristor crossbar-based analog circuit is designed to implement this neuro-fuzzy system which offers very interesting properties. In addition to high connectivity between neurons and being fault-tolerant, all synaptic weights in our proposed method are always non-negative and there is no need to precisely adjust them. Finally, this structure is hierarchically expandable and can compute operations in real time since it is implemented through analog circuits. Simulation results show the efficiency and applicability of our neuro-fuzzy computing system. They also indicate that this system can be a good candidate to be used for creating artificial brain.


GRASP and path-relinking for Coalition Structure Generation

Abstract: In Artificial Intelligence with Coalition Structure Generation (CSG) one refers to those cooperative complex problems that require to find an optimal partition, maximising a social welfare, of a set of entities involved in a system into exhaustive and disjoint coalitions. The solution of the CSG problem finds applications in many fields such as Machine Learning (covering machines, clustering), Data Mining (decision tree, discretization), Graph Theory, Natural Language Processing (aggregation), Semantic Web (service composition), and Bioinformatics. The problem of finding the optimal coalition structure is NP-complete. In this paper we present a greedy adaptive search procedure (GRASP) with path-relinking to efficiently search the space of coalition structures. Experiments and comparisons to other algorithms prove the validity of the proposed method in solving this hard combinatorial problem.


A Directional Feature with Energy based Offline Signature Verification Network

Abstract: Signature used as a biometric is implemented in various systems as well as every signature signed by each person is distinct at the same time. So, it is very important to have a computerized signature verification system. In offline signature verification system dynamic features are not available obviously, but one can use a signature as an image and apply image processing techniques to make an effective offline signature verification system. Author proposes a intelligent network used directional feature and energy density both as inputs to the same network and classifies the signature. Neural network is used as a classifier for this system. The results are compared with both the very basic energy density method and a simple directional feature method of offline signature verification system and this proposed new network is found very effective as compared to the above two methods, specially for less number of training samples, which can be implemented practically.


Framework to Solve Load Balancing Problem in Heterogeneous Web Servers

Abstract: For popular websites most important concern is to handle incoming load dynamically among web servers, so that they can respond to their client without any wait or failure. Different websites use different strategies to distribute load among web servers but most of the schemes concentrate on only one factor that is number of requests, but none of the schemes consider the point that different type of requests will require different level of processing efforts to answer, status record of all the web servers that are associated with one domain name and mechanism to handle a situation when one of the servers is not working. Therefore, there is a fundamental need to develop strategy for dynamic load allocation on web side. In this paper, an effort has been made to introduce a cluster based frame work to solve load distribution problem. This framework aims to distribute load among clusters on the basis of their operational capabilities. Moreover, the experimental results are shown with the help of example, algorithm and analysis of the algorithm.


Automatic Wrapper Adaptation by Tree Edit Distance Matching

Abstract: Information distributed through the Web keeps growing faster day by day, and for this reason, several techniques for extracting Web data have been suggested during last years. Often, extraction tasks are performed through so called wrappers, procedures extracting information from Web pages, e.g. implementing logic-based techniques. Many fields of application today require a strong degree of robustness of wrappers, in order not to compromise assets of information or reliability of data extracted. Unfortunately, wrappers may fail in the task of extracting data from a Web page, if its structure changes, sometimes even slightly, thus requiring the exploiting of new techniques to be automatically held so as to adapt the wrapper to the new structure of the page, in case of failure. In this work we present a novel approach of automatic wrapper adaptation based on the measurement of similarity of trees through improved tree edit distance matching techniques.


Design of Automatically Adaptable Web Wrappers

Abstract: Nowadays, the huge amount of information distributed through the Web motivates studying techniques to be adopted in order to extract relevant data in an efficient and reliable way. Both academia and enterprises developed several approaches of Web data extraction, for example using techniques of artificial intelligence or machine learning. Some commonly adopted procedures, namely wrappers, ensure a high degree of precision of information extracted from Web pages, and, at the same time, have to prove robustness in order not to compromise quality and reliability of data themselves. In this paper we focus on some experimental aspects related to the robustness of the data extraction process and the possibility of automatically adapting wrappers. We discuss the implementation of algorithms for finding similarities between two different version of a Web page, in order to handle modifications, avoiding the failure of data extraction tasks and ensuring reliability of information extracted. Our purpose is to evaluate performances, advantages and draw-backs of our novel system of automatic wrapper adaptation.


A General Framework for Representing, Reasoning and Querying with Annotated Semantic Web Data

Abstract: We describe a generic framework for representing and reasoning with annotated Semantic Web data, a task becoming more important with the recent increased amount of inconsistent and non-reliable meta-data on the web. We formalise the annotated language, the corresponding deductive system and address the query answering problem. Previous contributions on specific RDF annotation domains are encompassed by our unified reasoning formalism as we show by instantiating it on (i) temporal, (ii) fuzzy, and (iii) provenance annotations. Moreover, we provide a generic method for combining multiple annotation domains allowing to represent, e.g. temporally-annotated fuzzy RDF. Furthermore, we address the development of a query language -- AnQL -- that is inspired by SPARQL, including several features of SPARQL 1.1 (subqueries, aggregates, assignment, solution modifiers) along with the formal definitions of their semantics.


On the Cost of Concurrency in Transactional Memory

Abstract: The crux of software transactional memory (STM) is to combine an easy-to-use programming interface with an efficient utilization of the concurrent-computing abilities provided by modern machines. But does this combination come with an inherent cost? We evaluate the cost of concurrency by measuring the amount of expensive synchronization that must be employed in an STM implementation that ensures positive concurrency, i.e., allows for concurrent transaction processing in some executions. We focus on two popular progress conditions that provide positive concurrency: progressiveness and permissiveness. We show that in permissive STMs, providing a very high degree of concurrency, a transaction performs a linear number of expensive synchronization patterns with respect to its read-set size. In contrast, progressive STMs provide a very small degree of concurrency but, as we demonstrate, can be implemented using at most one expensive synchronization pattern per transaction. However, we show that even in progressive STMs, a transaction has to "protect" (e.g., by using locks or strong synchronization primitives) a linear amount of data with respect to its write-set size. Our results suggest that looking for high degrees of concurrency in STM implementations may bring a considerable synchronization cost.


Binary Sequent Calculi for Truth-invariance Entailment of Finite Many-valued Logics

Abstract: In this paper we consider the class of truth-functional many-valued logics with a finite set of truth-values. The main result of this paper is the development of a new \emph{binary} sequent calculi (each sequent is a pair of formulae) for many valued logic with a finite set of truth values, and of Kripke-like semantics for it that is both sound and complete. We did not use the logic entailment based on matrix with a strict subset of designated truth values, but a different new kind of semantics based on the generalization of the classic 2-valued truth-invariance entailment. In order to define this non-matrix based sequent calculi, we transform many-valued logic into positive 2-valued multi-modal logic with classic conjunction, disjunction and finite set of modal connectives. In this algebraic framework we define an uniquely determined axiom system, by extending the classic 2-valued distributive lattice logic (DLL) by a new set of sequent axioms for many-valued logic connectives. Dually, in an autoreferential Kripke-style framework we obtain a uniquely determined frame, where each possible world is an equivalence class of Lindenbaum algebra for a many-valued logic as well, represented by a truth value.


Around Dot-depth One

Abstract: The dot-depth hierarchy is a classification of star-free languages. It is related to the quantifier alternation hierarchy of first-order logic over finite words. We consider fragments of languages with dot-depth 1/2 and dot-depth 1 obtained by prohibiting the specification of prefixes or suffixes. As it turns out, these language classes are in one-to-one correspondence with fragments of existential first-order logic without min- or max-predicate. For all fragments, we obtain effective algebraic characterizations. Moreover, we give new combinatorial proofs for the decidability of the membership problem for dot-depth 1/2 and dot-depth 1.


An Analysis of Optimal Link Bombs

Abstract: We analyze the phenomenon of collusion for the purpose of boosting the pagerank of a node in an interlinked environment. We investigate the optimal attack pattern for a group of nodes (attackers) attempting to improve the ranking of a specific node (the victim). We consider attacks where the attackers can only manipulate their own outgoing links. We show that the optimal attacks in this scenario are uncoordinated, i.e. the attackers link directly to the victim and no one else. nodes do not link to each other. We also discuss optimal attack patterns for a group that wants to hide itself by not pointing directly to the victim. In these disguised attacks, the attackers link to nodes $l$ hops away from the victim. We show that an optimal disguised attack exists and how it can be computed. The optimal disguised attack also allows us to find optimal link farm configurations. A link farm can be considered a special case of our approach: the target page of the link farm is the victim and the other nodes in the link farm are the attackers for the purpose of improving the rank of the victim. The target page can however control its own outgoing links for the purpose of improving its own rank, which can be modeled as an optimal disguised attack of 1-hop on itself. Our results are unique in the literature as we show optimality not only in the pagerank score, but also in the rank based on the pagerank score. We further validate our results with experiments on a variety of random graph models.


A Secure Asynchronous FPGA Architecture, Experimental Results and Some Debug Feedback

Abstract: This article presents an asynchronous FPGA architecture for implementing cryptographic algorithms secured against physical cryptanalysis. We discuss the suitability of asynchronous reconfigurable architectures for such applications before proceeding to model the side channel and defining our objectives. The logic block architecture is presented in detail. We discuss several solutions for the interconnect architecture, and how these solutions can be ported to other flavours of interconnect (i.e. single driver). Next We discuss in detail a high speed asynchronous configuration chain architecture used to configure our asynchronous FPGA with simulation results, and we present a 3 X 3 prototype FPGA fabricated in 65 nm CMOS. Lastly we present experiments to test the high speed asynchronous configuration chain and evaluate how far our objectives have been achieved with proposed solutions, and we conclude with emphasis on complementary FPGA CAD algorithms, and the effect of CMOS variation on Side-Channel Vulnerability.


Higher-Order Symbolic Execution via Contracts

Abstract: We present a new approach to automated reasoning about higher-order programs by extending symbolic execution to use behavioral contracts as symbolic values, enabling symbolic approximation of higher-order behavior. Our approach is based on the idea of an abstract reduction semantics that gives an operational semantics to programs with both concrete and symbolic components. Symbolic components are approximated by their contract and our semantics gives an operational interpretation of contracts-as-values. The result is a executable semantics that soundly predicts program behavior, including contract failures, for all possible instantiations of symbolic components. We show that our approach scales to an expressive language of contracts including arbitrary programs embedded as predicates, dependent function contracts, and recursive contracts. Supporting this feature-rich language of specifications leads to powerful symbolic reasoning using existing program assertions. We then apply our approach to produce a verifier for contract correctness of components, including a sound and computable approximation to our semantics that facilitates fully automated contract verification. Our implementation is capable of verifying contracts expressed in existing programs, and of justifying valuable contract-elimination optimizations.


Efficient Batch Query Answering Under Differential Privacy

Abstract: Differential privacy is a rigorous privacy condition achieved by randomizing query answers. This paper develops efficient algorithms for answering multiple queries under differential privacy with low error. We pursue this goal by advancing a recent approach called the matrix mechanism, which generalizes standard differentially private mechanisms. This new mechanism works by first answering a different set of queries (a strategy) and then inferring the answers to the desired workload of queries. Although a few strategies are known to work well on specific workloads, finding the strategy which minimizes error on an arbitrary workload is intractable. We prove a new lower bound on the optimal error of this mechanism, and we propose an efficient algorithm that approaches this bound for a wide range of workloads.


Approximation Algorithms for Maximum Independent Set of Pseudo-Disks

Abstract: We present approximation algorithms for maximum independent set of pseudo-disks in the plane, both in the weighted and unweighted cases. For the unweighted case, we prove that a local search algorithm yields a \PTAS. For the weighted case, we suggest a novel rounding scheme based on an \LP relaxation of the problem, which leads to a constant-factor approximation. Most previous algorithms for maximum independent set (in geometric settings) relied on packing arguments that are not applicable in this case. As such, the analysis of both algorithms requires some new combinatorial ideas, which we believe to be of independent interest.


A Semi-Automatic Graph-Based Approach for Determining the Boundary of Eloquent Fiber Bundles in the Human Brain

Abstract: Diffusion Tensor Imaging (DTI) allows estimating the position, orientation and dimension of bundles of nerve pathways. This non-invasive imaging technique takes advantage of the diffusion of water molecules and determines the diffusion coefficients for every voxel of the data set. The identification of the diffusion coefficients and the derivation of information about fiber bundles is of major interest for planning and performing neurosurgical interventions. To minimize the risk of neural deficits during brain surgery as tumor resection (e.g. glioma), the segmentation and integration of the results in the operating room is of prime importance. In this contribution, a robust and efficient graph-based approach for segmentating tubular fiber bundles in the human brain is presented. To define a cost function, the fractional anisotropy (FA) is used, derived from the DTI data, but this value may differ from patient to patient. Besides manually definining seed regions describing the structure of interest, additionally a manual definition of the cost function by the user is necessary. To improve the approach the contribution introduces a solution for automatically determining the cost function by using different 3D masks for each individual data set.


The Planetary System: Executable Science, Technology, Engineering and Math Papers

Abstract: Executable scientific papers contain not just layouted text for reading. They contain, or link to, machine-comprehensible representations of the scientific findings or experiments they describe. Client-side players can thus enable readers to "check, manipulate and explore the result space". We have realized executable papers in the STEM domain with the Planetary system. Semantic annotations associate the papers with a content commons holding the background ontology, the annotations are exposed as Linked Data, and a frontend player application hooks modular interactive services into the semantic annotations.


Drag and Drop: Influences on the Design of Reusable Software Components

Abstract: The fundamental unit of large scale software construction is the component. A component is the fundamental user interface object in Java. Everything you see on the display in a java application is a component. The ability to let users drag a component from the Interface and drop into your application is almost a requirement of a modern, commercial user interface. The CBD approach brings high component reusability and easy maintainability, and reduces time-to-market. This paper describes the component repository which provides functionality for component reuse process through the drag and drop mechanism and it's influences on the reusable components


An Exact Algorithm for Side-Chain Placement in Protein Design

Abstract: Computational protein design aims at constructing novel or improved functions on the structure of a given protein backbone and has important applications in the pharmaceutical and biotechnical industry. The underlying combinatorial side-chain placement problem consists of choosing a side-chain placement for each residue position such that the resulting overall energy is minimum. The choice of the side-chain then also determines the amino acid for this position. Many algorithms for this NP-hard problem have been proposed in the context of homology modeling, which, however, reach their limits when faced with large protein design instances. In this paper, we propose a new exact method for the side-chain placement problem that works well even for large instance sizes as they appear in protein design. Our main contribution is a dedicated branch-and-bound algorithm that combines tight upper and lower bounds resulting from a novel Lagrangian relaxation approach for side-chain placement. Our experimental results show that our method outperforms alternative state-of-the art exact approaches and makes it possible to optimally solve large protein design instances routinely.


Climbing depth-bounded adjacent discrepancy search for solving hybrid flow shop scheduling problems with multiprocessor tasks

Abstract: This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The problem even in its simplest form is NP-hard in the strong sense. The great deal of interest for this problem, besides its theoretical complexity, is animated by needs of various manufacturing and computing systems. We propose a new approach based on limited discrepancy search to solve the problem. Our method is tested with reference to a proposed lower bound as well as the best-known solutions in literature. Computational results show that the developed approach is efficient in particular for large-size problems.


One Bad Apple Spoils the Bunch: Exploiting P2P Applications to Trace and Profile Tor Users

Abstract: Tor is a popular low-latency anonymity network. However, Tor does not protect against the exploitation of an insecure application to reveal the IP address of, or trace, a TCP stream. In addition, because of the linkability of Tor streams sent together over a single circuit, tracing one stream sent over a circuit traces them all. Surprisingly, it is unknown whether this linkability allows in practice to trace a significant number of streams originating from secure (i.e., proxied) applications. In this paper, we show that linkability allows us to trace 193% of additional streams, including 27% of HTTP streams possibly originating from "secure" browsers. In particular, we traced 9% of Tor streams carried by our instrumented exit nodes. Using BitTorrent as the insecure application, we design two attacks tracing BitTorrent users on Tor. We run these attacks in the wild for 23 days and reveal 10,000 IP addresses of Tor users. Using these IP addresses, we then profile not only the BitTorrent downloads but also the websites visited per country of origin of Tor users. We show that BitTorrent users on Tor are over-represented in some countries as compared to BitTorrent users outside of Tor. By analyzing the type of content downloaded, we then explain the observed behaviors by the higher concentration of pornographic content downloaded at the scale of a country. Finally, we present results suggesting the existence of an underground BitTorrent ecosystem on Tor.


The tractability of CSP classes defined by forbidden patterns

Abstract: The constraint satisfaction problem (CSP) is a general problem central to computer science and artificial intelligence. Although the CSP is NP-hard in general, considerable effort has been spent on identifying tractable subclasses. The main two approaches consider structural properties (restrictions on the hypergraph of constraint scopes) and relational properties (restrictions on the language of constraint relations). Recently, some authors have considered hybrid properties that restrict the constraint hypergraph and the relations simultaneously. Our key contribution is the novel concept of a CSP pattern and classes of problems defined by forbidden patterns (which can be viewed as forbidding generic subproblems). We describe the theoretical framework which can be used to reason about classes of problems defined by forbidden patterns. We show that this framework generalises relational properties and allows us to capture known hybrid tractable classes. Although we are not close to obtaining a dichotomy concerning the tractability of general forbidden patterns, we are able to make some progress in a special case: classes of problems that arise when we can only forbid binary negative patterns (generic subproblems in which only inconsistent tuples are specified). In this case we are able to characterise very large classes of tractable and NP-hard forbidden patterns. This leaves the complexity of just one case unresolved and we conjecture that this last case is tractable.


Cost Sharing in Social Community Networks

Abstract: Wireless social community networks (WSCNs) is an emerging technology that operate in the unlicensed spectrum and have been created as an alternative to cellular wireless networks for providing low-cost, high speed wireless data access in urban areas. WSCNs is an upcoming idea that is starting to gain attention amongst the civilian Internet users. By using \emph{special} WiFi routers that are provided by a social community network provider (SCNP), users can effectively share their connection with the neighborhood in return for some monthly monetary benefits. However, deployment maps of existing WSCNs reflect their slow progress in capturing the WiFi router market. In this paper, we look at a router design and cost sharing problem in WSCNs to improve deployment. We devise asimple to implement, successful a mechanism is successful if it achieves its intended purpose. For example in this work, a successful mechanism would help install routers in a locality}, \emph{budget-balanced}, \emph{ex-post efficient}, and \emph{individually rational} {a mechanism is individually rational if the benefit each agent obtains is greater than its cost.} auction-based mechanism that generates the \emph{optimal} number of features a router should have and allocates costs to residential users in \emph{proportion} to the feature benefits they receive. Our problem is important to a new-entrant SCNP when it wants to design its multi-feature routers with the goal to popularize them and increase their deployment in a residential locality. Our proposed mechanism accounts for heterogeneous user preferences towards different router features and comes up with the optimal \emph{(feature-set, user costs)} router blueprint that satisfies each user in a locality, in turn motivating them to buy routers and thereby improve deployment.


Pricing and Investments in Internet Security: A Cyber-Insurance Perspective

Abstract: Internet users such as individuals and organizations are subject to different types of epidemic risks such as worms, viruses, spams, and botnets. To reduce the probability of risk, an Internet user generally invests in traditional security mechanisms like anti-virus and anti-spam software, sometimes also known as self-defense mechanisms. However, such software does not completely eliminate risk. Recent works have considered the problem of residual risk elimination by proposing the idea of cyber-insurance. In this regard, an important research problem is the analysis of optimal user self-defense investments and cyber-insurance contracts under the Internet environment. In this paper, we investigate two problems and their relationship: 1) analyzing optimal self-defense investments in the Internet, under optimal cyber-insurance coverage, where optimality is an insurer objective and 2) designing optimal cyber-insurance contracts for Internet users, where a contract is a (premium, coverage) pair.


All Roads Lead To Rome

Abstract: This short article presents a class of projection-based solution algorithms to the problem considered in the pioneering work on compressed sensing - perfect reconstruction of a phantom image from 22 radial lines in the frequency domain. Under the framework of projection-based image reconstruction, we will show experimentally that several old and new tools of nonlinear filtering (including Perona-Malik diffusion, nonlinear diffusion, Translation-Invariant thresholding and SA-DCT thresholding) all lead to perfect reconstruction of the phantom image.


On Minimal Constraint Networks

Abstract: In a minimal binary constraint network, every tuple of a constraint relation can be extended to a solution. The tractability or intractability of computing a solution to such a minimal network was a long standing open question. Dechter conjectured this computation problem to be NP-hard. We prove this conjecture. We also prove a conjecture by Dechter and Pearl stating that for k\geq2 it is NP-hard to decide whether a single constraint can be decomposed into an equivalent k-ary constraint network. We show that this holds even in case of bi-valued constraints where k\geq3, which proves another conjecture of Dechter and Pearl. Finally, we establish the tractability frontier for this problem with respect to the domain cardinality and the parameter k.


Decidability and Shortest Strings in Formal Languages

Abstract: Given a formal language L specified in various ways, we consider the problem of determining if L is nonempty. If L is indeed nonempty, we find upper and lower bounds on the length of the shortest string in L.


A Gentle Introduction to the Kernel Distance

Abstract: This document reviews the definition of the kernel distance, providing a gentle introduction tailored to a reader with background in theoretical computer science, but limited exposure to technology more common to machine learning, functional analysis and geometric measure theory. The key aspect of the kernel distance developed here is its interpretation as an L_2 distance between probability measures or various shapes (e.g. point sets, curves, surfaces) embedded in a vector space (specifically an RKHS). This structure enables several elegant and efficient solutions to data analysis problems. We conclude with a glimpse into the mathematical underpinnings of this measure, highlighting its recent independent evolution in two separate fields.


Planning Graph Heuristics for Belief Space Search

Abstract: Some recent works in conditional planning have proposed reachability heuristics to improve planner scalability, but many lack a formal description of the properties of their distance estimates. To place previous work in context and extend work on heuristics for conditional planning, we provide a formal basis for distance estimates between belief states. We give a definition for the distance between belief states that relies on aggregating underlying state distance measures. We give several techniques to aggregate state distances and their associated properties. Many existing heuristics exhibit a subset of the properties, but in order to provide a standardized comparison we present several generalizations of planning graph heuristics that are used in a single planner. We compliment our belief state distance estimate framework by also investigating efficient planning graph data structures that incorporate BDDs to compute the most effective heuristics. We developed two planners to serve as test-beds for our investigation. The first, CAltAlt, is a conformant regression planner that uses A* search. The second, POND, is a conditional progression planner that uses AO* search. We show the relative effectiveness of our heuristic techniques within these planners. We also compare the performance of these planners with several state of the art approaches in conditional planning.


Authentication planning for XOR network coding

Abstract: This paper formulates the authentication planning problem when network coding is implemented in a wireless sensor network. The planning problem aims at minimizing the energy consumed by the security application which is guarantied using message authentication codes. This paper proposes a binary non-linear optimization formulation for this planning problem whose decision variables are the authentication decision of the nodes and the MAC modes of operation. It is illustrated for a butterfly topology. Results show that there is a real trade-off between energy efficiency and message throughput in this context.


Efficient and Playful Tools to Teach Unix to New Students

Abstract: Teaching Unix to new students is a common tasks in many higher schools. This paper presents an approach to such course where the students progress autonomously with the help of the teacher. The traditional textbook is complemented with a wiki, and the main thread of the course is a game, in the form of a treasure hunt. The course finishes with a lab exam, where students have to perform practical manipulations similar to the ones performed during the treasure hunt. The exam is graded fully automatically. This paper discusses the motivations and advantages of the approach, and gives an overall view of the tools we developed. The tools are available from the web, and open-source, hence re-usable outside the Ensimag.


Adding Network Coding Capabilities to the WSNet Simulator

Abstract: This technical report presents the implementation of a Network Coding module in WSNet - a Wireless Sensor Network simulator. This implementation provides a generic programming interface to allow an easy specialization of different coding strategies: random, source/destination-oriented, intra/inter-flow, etc.


An efficient multi-use multi-secret sharing scheme based on hash function

Abstract: In this paper, a renewable, multi-use, multi-secret sharing scheme for general access structure based on one-way collision resistant hash function is presented in which each participant has to carry only one share. By applying collision-resistant one-way hash function, the proposed scheme is secure against conspiracy attacks even if the pseudo-secret shares are compromised. Moreover, high complexity operations like modular multiplication, exponentiation and inversion are avoided to increase its efficiency. Finally, in the proposed scheme, both the combiner and the participants can verify the correctness of the information exchanged among themselves.


Efficient Algorithms for Distributed Detection of Holes and Boundaries in Wireless Networks

Abstract: We propose two novel algorithms for distributed and location-free boundary recognition in wireless sensor networks. Both approaches enable a node to decide autonomously whether it is a boundary node, based solely on connectivity information of a small neighborhood. This makes our algorithms highly applicable for dynamic networks where nodes can move or become inoperative. We compare our algorithms qualitatively and quantitatively with several previous approaches. In extensive simulations, we consider various models and scenarios. Although our algorithms use less information than most other approaches, they produce significantly better results. They are very robust against variations in node degree and do not rely on simplified assumptions of the communication model. Moreover, they are much easier to implement on real sensor nodes than most existing approaches.


Non-Boolean almost perfect nonlinear functions on non-Abelian groups

Abstract: The purpose of this paper is to present the extended definitions and characterizations of the classical notions of APN and maximum nonlinear Boolean functions to deal with the case of mappings from a finite group K to another one N with the possibility that one or both groups are non-Abelian.


Side-Channel Oscilloscope

Abstract: Side-Channel Analysis used for codebreaking could be used constructively as a probing tool for internal gates in integrated circuits. This paper outlines basic methods and mathematics for that purpose


Recognizing Uncertainty in Speech

Abstract: We address the problem of inferring a speaker's level of certainty based on prosodic information in the speech signal, which has application in speech-based dialogue systems. We show that using phrase-level prosodic features centered around the phrases causing uncertainty, in addition to utterance-level prosodic features, improves our model's level of certainty classification. In addition, our models can be used to predict which phrase a person is uncertain about. These results rely on a novel method for eliciting utterances of varying levels of certainty that allows us to compare the utility of contextually-based feature sets. We elicit level of certainty ratings from both the speakers themselves and a panel of listeners, finding that there is often a mismatch between speakers' internal states and their perceived states, and highlighting the importance of this distinction.


Hereditary biclique-Helly graphs: recognition and maximal biclique enumeration

Abstract: A biclique is a set of vertices that induce a bipartite complete graph. A graph G is biclique-Helly when its family of maximal bicliques satisfies the Helly property. If every induced subgraph of G is also biclique-Helly, then G is hereditary biclique-Helly. A graph is C_4-dominated when every cycle of length 4 contains a vertex that is dominated by the vertex of the cycle that is not adjacent to it. In this paper we show that the class of hereditary biclique-Helly graphs is formed precisely by those C_4-dominated graphs that contain no triangles and no induced cycles of length either 5, or 6. Using this characterization, we develop an algorithm for recognizing hereditary biclique-Helly graphs in O(n^2+\alpha m) time and O(m) space. (Here n, m, and \alpha = O(m^{1/2}) are the number of vertices and edges, and the arboricity of the graph, respectively.) As a subprocedure, we show how to recognize those C_4-dominated graphs that contain no triangles in O(\alpha m) time and O(m) space. Finally, we show how to enumerate all the maximal bicliques of a C_4-dominated graph with no triangles in O(n^2 + \alpha m) time and O(\alpha m) space, and we discuss how some biclique problems can be solved in O(\alpha m) time and O(n+m) space.


Ray-Based and Graph-Based Methods for Fiber Bundle Boundary Estimation

Abstract: Diffusion Tensor Imaging (DTI) provides the possibility of estimating the location and course of eloquent structures in the human brain. Knowledge about this is of high importance for preoperative planning of neurosurgical interventions and for intraoperative guidance by neuronavigation in order to minimize postoperative neurological deficits. Therefore, the segmentation of these structures as closed, three-dimensional object is necessary. In this contribution, two methods for fiber bundle segmentation between two defined regions are compared using software phantoms (abstract model and anatomical phantom modeling the right corticospinal tract). One method uses evaluation points from sampled rays as candidates for boundary points, the other method sets up a directed and weighted (depending on a scalar measure) graph and performs a min-cut for optimal segmentation results. Comparison is done by using the Dice Similarity Coefficient (DSC), a measure for spatial overlap of different segmentation results.


Development of Computer Science Disciplines - A Social Network Analysis Approach

Abstract: In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published JCR (Journal Citation Report). Although this data covers most of important journals, it lacks computer science conference and workshop proceedings. That results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investigate the collaborative and citation behavior of journals/conferences by analyzing the properties of their co-authorship and citation subgraphs. The paper draws several important conclusions. First, conferences constitute social structures that shape the computer science knowledge. Second, computer science is becoming more interdisciplinary. Third, experts are the key success factor for sustainability of journals/conferences.


Symbolic Execution for Verification

Abstract: In previous work, we presented a symbolic execution method which starts with a concrete model of the program but progressively abstracts away details only when these are known to be irrelevant using interpolation. In this paper, we extend the technique to handle unbounded loops. The central idea is to progressively discover the strongest invariants through a process of loop unrolling. The key feature of this technique, called the minimax algorithm, is intelligent backtracking which directs the search for the next invariant. We then present an analysis of the main differences between our symbolic execution method and mainstream techniques mainly based on abstract refinement (CEGAR). Finally, we evaluate our technique against available state-of-the-art systems.


Augmented reality usage for prototyping speed up

Abstract: The first part of the article describes our approach for solution of this problem by means of Augmented Reality. The merging of the real world model and digital objects allows streamline the work with the model and speed up the whole production phase significantly. The main advantage of augmented reality is the possibility of direct manipulation with the scene using a portable digital camera. Also adding digital objects into the scene could be done using identification markers placed on the surface of the model. Therefore it is not necessary to work with special input devices and lose the contact with the real world model. Adjustments are done directly on the model. The key problem of outlined solution is the ability of identification of an object within the camera picture and its replacement with the digital object. The second part of the article is focused especially on the identification of exact position and orientation of the marker within the picture. The identification marker is generalized into the triple of points which represents a general plane in space. There is discussed the space identification of these points and the description of representation of their position and orientation be means of transformation matrix. This matrix is used for rendering of the graphical objects (e. g. in OpenGL and Direct3D).


An Artificial Immune System Model for Multi-Agents Resource Sharing in Distributed Environments

Abstract: Natural Immune system plays a vital role in the survival of the all living being. It provides a mechanism to defend itself from external predates making it consistent systems, capable of adapting itself for survival incase of changes. The human immune system has motivated scientists and engineers for finding powerful information processing algorithms that has solved complex engineering tasks. This paper explores one of the various possibilities for solving problem in a Multiagent scenario wherein multiple robots are deployed to achieve a goal collectively. The final goal is dependent on the performance of individual robot and its survival without having to lose its energy beyond a predetermined threshold value by deploying an evolutionary computational technique otherwise called the artificial immune system that imitates the biological immune system.


A Randomized Algorithm Based on Threshold Accepting to Approximate the Star Discrepancy

Abstract: We present a new algorithm for estimating the star discrepancy of arbitrary point sets. Similar to the algorithm for discrepancy approximation of Winker and Fang [SIAM J. Numer. Anal. 34 (1997), 2028--2042] it is based on the optimization algorithm threshold accepting. Our improvements include, amongst others, a non-uniform sampling strategy which is more suited for higher-dimensional inputs, and rounding steps which transform axis-parallel boxes, on which the discrepancy is to be tested, into \emph{critical test boxes}. These critical test boxes provably yield higher discrepancy values, and contain the box that exhibits the maximum value of the local discrepancy. We provide comprehensive experiments to test the new algorithm. Our randomized algorithm computes the exact discrepancy frequently in all cases where this can be checked (i.e., where the exact discrepancy of the point set can be computed in feasible time). Most importantly, in higher dimension the new method behaves clearly better than all previously known methods.


A hybrid model for bankruptcy prediction using genetic algorithm, fuzzy c-means and mars

Abstract: Bankruptcy prediction is very important for all the organization since it affects the economy and rise many social problems with high costs. There are large number of techniques have been developed to predict the bankruptcy, which helps the decision makers such as investors and financial analysts. One of the bankruptcy prediction models is the hybrid model using Fuzzy C-means clustering and MARS, which uses static ratios taken from the bank financial statements for prediction, which has its own theoretical advantages. The performance of existing bankruptcy model can be improved by selecting the best features dynamically depend on the nature of the firm. This dynamic selection can be accomplished by Genetic Algorithm and it improves the performance of prediction model.


3-SAT Faster and Simpler - Unique-SAT Bounds for PPSZ Hold in General

Abstract: The PPSZ algorithm by Paturi, Pudl\'ak, Saks, and Zane [1998] is the fastest known algorithm for Unique k-SAT, where the input formula does not have more than one satisfying assignment. For k>=5 the same bounds hold for general k-SAT. We show that this is also the case for k=3,4, using a slightly modified PPSZ algorithm. We do the analysis by defining a cost for satisfiable CNF formulas, which we prove to decrease in each PPSZ step by a certain amount. This improves our previous best bounds with Moser and Scheder [2011] for 3-SAT to O(1.308^n) and for 4-SAT to O(1.469^n).


Improved space-time tradeoffs for approximate full-text indexing with one edit error

Abstract: In this paper we are interested in indexing texts for substring matching queries with one edit error. That is, given a text $T$ of $n$ characters over an alphabet of size $\sigma$, we are asked to build a data structure that answers the following query: find all the $occ$ substrings of the text that are at edit distance at most $1$ from a given string $q$ of length $m$. In this paper we show two new results for this problem. The first result, suitable for an unbounded alphabet, uses $O(n\log^\epsilon n)$ (where $\epsilon$ is any constant such that $0<\epsilon<1$) words of space and answers to queries in time $O(m+occ)$. This improves simultaneously in space and time over the result of Cole et al. The second result, suitable only for a constant alphabet, relies on compressed text indices and comes in two variants: the first variant uses $O(n\log^{\epsilon} n)$ bits of space and answers to queries in time $O(m+occ)$, while the second variant uses $O(n\log\log n)$ bits of space and answers to queries in time $O((m+occ)\log\log n)$. This second result improves on the previously best results for constant alphabets achieved in Lam et al. (Algorithmica 2008) and Chan et al. (Algorithmica 2010).


Stability and Queueing Analysis of IEEE 802.11 Distributed Coordination Function

Abstract: A widely adopted two-dimensional Markov chain model of the IEEE 802.11 DCF was introduced by Bianchi to characterize the backoff behavior of a single node under a saturated traffic condition. Using this approach, we propose a queuing model for the 802.11 DCF under a non-saturated traffic environment. The input buffer of each node is modeled as a Geo/G/1 queue, and the packet service time distribution is derived from Markov state space of 802.11 DCF with the underlying scheduling algorithm. The DCF defines two access mechanisms, namely the Basic access mechanism and the request-to-send/clear-to-send (RTS/CTS) access mechanism. Based on our model, performance analyses of both schemes are studied with probabilistic exponential backoff scheduling. We obtain the characteristic equation of network throughput and expressions of packet queueing delay. Specifically, we obtain the stable throughput and bounded delay regions with respect to the retransmission factor according to the basic queueing analysis. For both access schemes, the bounded delay region is a subset of the stable throughput region. Our results show that the RTS/CTS access mechanism is more stable and performs better than the Basic access mechanism. The analysis in this paper is verified by simulation results.


Trust beyond reputation: A computational trust model based on stereotypes

Abstract: Models of computational trust support users in taking decisions. They are commonly used to guide users' judgements in online auction sites; or to determine quality of contributions in Web 2.0 sites. However, most existing systems require historical information about the past behavior of the specific agent being judged. In contrast, in real life, to anticipate and to predict a stranger's actions in absence of the knowledge of such behavioral history, we often use our "instinct"- essentially stereotypes developed from our past interactions with other "similar" persons. In this paper, we propose StereoTrust, a computational trust model inspired by stereotypes as used in real-life. A stereotype contains certain features of agents and an expected outcome of the transaction. When facing a stranger, an agent derives its trust by aggregating stereotypes matching the stranger's profile. Since stereotypes are formed locally, recommendations stem from the trustor's own personal experiences and perspective. Historical behavioral information, when available, can be used to refine the analysis. According to our experiments using Epinions.com dataset, StereoTrust compares favorably with existing trust models that use different kinds of information and more complete historical information.


Control Complexity in Bucklin and Fallback Voting

Abstract: Electoral control models ways of changing the outcome of an election via such actions as adding/deleting/partitioning either candidates or voters. To protect elections from such control attempts, computational complexity has been investigated and the corresponding NP-hardness results are termed "resistance." It has been a long-running project of research in this area to classify the major voting systems in terms of their resistance properties. We show that fallback voting, an election system proposed by Brams and Sanver (2009) to combine Bucklin with approval voting, is resistant to each of the common types of control except to destructive control by either adding or deleting voters. Thus fallback voting displays the broadest control resistance currently known to hold among natural election systems with a polynomial-time winner problem. We also study the control complexity of Bucklin voting and show that it performs at least almost as well as fallback voting in terms of control resistance. As Bucklin voting is a special case of fallback voting, each resistance shown for Bucklin voting strengthens the corresponding resistance for fallback voting. Such worst-case complexity analysis is at best an indication of security against control attempts, rather than a proof. In practice, the difficulty of control will depend on the structure of typical instances. We investigate the parameterized control complexity of Bucklin and fallback voting, according to several parameters that are often likely to be small for typical instances. Our results, though still in the worst-case complexity model, can be interpreted as significant strengthenings of the resistance demonstrations based on NP-hardness.


Formal verification of a time-triggered hardware interface

Abstract: We present a formal proof of a time-triggered hardware interface. The design implements the bit-clock synchronization mechanism specified by the FlexRay standard for automotive embedded systems. The design is described at the gate-level. It can be translated to Verilog and synthesized on FPGA. The proof is based on a general model of asynchronous communications and combines interactive theorem proving in Isabelle/HOL and automatic model-checking using NuSMV together with a model-reduction procedure, IHaVeIt. Our general model of asynchronous communications defines a clear separation between analog and digital concerns. This separation enables the combination of theorem proving and model-checking for an efficient methodology. The analog phenomena are formalized in the logic of Isabelle/HOL. The gate-level hardware is automatically analyzed using IHaVeIt. Our proof reveals the correct values of a crucial parameter of the bit-clock synchronization mechanism. Our main theorem proves the functional correctness as well as the maximum number of cycles of the transmission.


Channel Assignment via Fast Zeta Transform

Abstract: We show an O*((l+1)^n)-time algorithm for the channel assignment problem, where l is the maximum edge weight. This improves on the previous O*((l+2)^n)-time algorithm by Kral, as well as algorithms for important special cases, like L(2,1)-labelling. For the latter problem, our algorithm works in O*(3^n) time. The progress is achieved by applying the fast zeta transform in combination with the inclusion-exclusion principle.


FavourQueue: a Parameterless Active Queue Management to Speed Up Short TCP Flows (and others too!)

Abstract: This paper presents and analyses the implementation of a novel active queue management (AQM) named FavorQueue that aims to improve delay transfer of short lived TCP flows over best-effort networks. The idea is to dequeue packets that do not belong to a flow previously enqueued first. The rationale is to mitigate the delay induced by long-lived TCP flows over the pace of short TCP data requests and to prevent dropped packets at the beginning of a connection and during recovery period. Although the main target of this AQM is to accelerate short TCP traffic, we show that FavorQueue does not only improve the performance of short TCP traffic but also improves the performance of all TCP traffic in terms of drop ratio and latency whatever the flow size. In particular, we demonstrate that FavorQueue reduces the loss of a retransmitted packet, decreases the number of dropped packets recovered by RTO and improves the latency up to 30% compared to DropTail. Finally, we show that this scheme remains compliant with recent TCP updates such as the increase of the initial slow-start value.


Building XenoBuntu Linux Distribution for Teaching and Prototyping Real-Time Operating Systems

Abstract: This paper describes the realization of a new Linux distribution based on Ubuntu Linux and Xenomai Real-Time framework. This realization is motivated by the eminent need of real-time systems in modern computer science courses. The majority of the technical choices are made after qualitative comparison. The main goal of this distribution is to offer standard Operating Systems (OS) that include Xenomai infrastructure and the essential tools to begin hard real-time application development inside a convivial desktop environment. The released live/installable DVD can be adopted to emulate several classic RTOS Application Program Interfaces (APIs), directly use and understand real-time Linux in convivial desktop environment and prototyping real-time embedded applications.


SPPAM - Statistical PreProcessing AlgorithM

Abstract: Most machine learning tools work with a single table where each row is an instance and each column is an attribute. Each cell of the table contains an attribute value for an instance. This representation prevents one important form of learning, which is, classification based on groups of correlated records, such as multiple exams of a single patient, internet customer preferences, weather forecast or prediction of sea conditions for a given day. To some extent, relational learning methods, such as inductive logic programming, can capture this correlation through the use of intensional predicates added to the background knowledge. In this work, we propose SPPAM, an algorithm that aggregates past observations in one single record. We show that applying SPPAM to the original correlated data, before the learning task, can produce classifiers that are better than the ones trained using all records.


Transparent Programming of Heterogeneous Smartphones for Sensing

Abstract: Sensing on smartphones is known to be power-hungry. It has been shown that this problem can be solved by adding an ultra low-power processor to execute simple, frequent sensor data processing. While very effective in saving energy, this resulting heterogeneous, distributed architecture poses a significant challenge to application development. We present Reflex, a suite of runtime and compilation techniques to conceal the heterogeneous, distributed nature from developers. The Reflex automatically transforms the developer's code for distributed execution with the help of the Reflex runtime. To create a unified system illusion, Reflex features a novel software distributed shared memory (DSM) design that leverages the extreme architectural asymmetry between the low-power processor and the powerful central processor to achieve both energy efficiency and performance. We report a complete realization of Reflex for heterogeneous smartphones with Maemo/Linux as the central kernel. Using a tri-processor hardware prototype and sensing applications reported in recent literature, we evaluate the Reflex realization for programming transparency, energy efficiency, and performance. We show that Reflex supports a programming style that is very close to contemporary smartphone programming. It allows existing sensing applications to be ported with minor source code changes. Reflex reduces the system power in sensing by up to 83%, and its runtime system only consumes 10% local memory on a typical ultra-low power processor.


Language, Emotions, and Cultures: Emotional Sapir-Whorf Hypothesis

Abstract: An emotional version of Sapir-Whorf hypothesis suggests that differences in language emotionalities influence differences among cultures no less than conceptual differences. Conceptual contents of languages and cultures to significant extent are determined by words and their semantic differences; these could be borrowed among languages and exchanged among cultures. Emotional differences, as suggested in the paper, are related to grammar and mostly cannot be borrowed. Conceptual and emotional mechanisms of languages are considered here along with their functions in the mind and cultural evolution. A fundamental contradiction in human mind is considered: language evolution requires reduced emotionality, but "too low" emotionality makes language "irrelevant to life," disconnected from sensory-motor experience. Neural mechanisms of these processes are suggested as well as their mathematical models: the knowledge instinct, the language instinct, the dual model connecting language and cognition, dynamic logic, neural modeling fields. Mathematical results are related to cognitive science, linguistics, and psychology. Experimental evidence and theoretical arguments are discussed. Approximate equations for evolution of human minds and cultures are obtained. Their solutions identify three types of cultures: "conceptual"-pragmatic cultures, in which emotionality of language is reduced and differentiation overtakes synthesis resulting in fast evolution at the price of uncertainty of values, self doubts, and internal crises; "traditional-emotional" cultures where differentiation lags behind synthesis, resulting in cultural stability at the price of stagnation; and "multi-cultural" societies combining fast cultural evolution and stability. Unsolved problems and future theoretical and experimental directions are discussed.


Data-centric Misbehavior Detection in VANETs

Abstract: Detecting misbehavior (such as transmissions of false information) in vehicular ad hoc networks (VANETs) is very important problem with wide range of implications including safety related and congestion avoidance applications. We discuss several limitations of existing misbehavior detection schemes (MDS) designed for VANETs. Most MDS are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners, e.g. for gaining access to a particular lane. Because of this (\emph{rational behavior}), it is more important to detect false information than to identify misbehaving nodes. We introduce the concept of data-centric misbehavior detection and propose algorithms which detect false alert messages and misbehaving nodes by observing their actions after sending out the alert messages. With the data-centric MDS, each node can independently decide whether an information received is correct or false. The decision is based on the consistency of recent messages and new alert with reported and estimated vehicle positions. No voting or majority decisions is needed, making our MDS resilient to Sybil attacks. Instead of revoking all the secret credentials of misbehaving nodes, as done in most schemes, we impose fines on misbehaving nodes (administered by the certification authority), discouraging them to act selfishly. This reduces the computation and communication costs involved in revoking all the secret credentials of misbehaving nodes.


Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining

Abstract: Scaling up the sparse matrix-vector multiplication kernel on modern Graphics Processing Units (GPU) has been at the heart of numerous studies in both academia and industry. In this article we present a novel non-parametric, self-tunable, approach to data representation for computing this kernel, particularly targeting sparse matrices representing power-law graphs. Using real data, we show how our representation scheme, coupled with a novel tiling algorithm, can yield significant benefits over the current state of the art GPU efforts on a number of core data mining algorithms such as PageRank, HITS and Random Walk with Restart.


Automatic Wrappers for Large Scale Web Extraction

Abstract: We present a generic framework to make wrapper induction algorithms tolerant to noise in the training data. This enables us to learn wrappers in a completely unsupervised manner from automatically and cheaply obtained noisy training data, e.g., using dictionaries and regular expressions. By removing the site-level supervision that wrapper-based techniques require, we are able to perform information extraction at web-scale, with accuracy unattained with existing unsupervised extraction techniques. Our system is used in production at Yahoo! and powers live applications.


Using Paxos to Build a Scalable, Consistent, and Highly Available Datastore

Abstract: Spinnaker is an experimental datastore that is designed to run on a large cluster of commodity servers in a single datacenter. It features key-based range partitioning, 3-way replication, and a transactional get-put API with the option to choose either strong or timeline consistency on reads. This paper describes Spinnaker's Paxos-based replication protocol. The use of Paxos ensures that a data partition in Spinnaker will be available for reads and writes as long a majority of its replicas are alive. Unlike traditional master-slave replication, this is true regardless of the failure sequence that occurs. We show that Paxos replication can be competitive with alternatives that provide weaker consistency guarantees. Compared to an eventually consistent datastore, we show that Spinnaker can be as fast or even faster on reads and only 5% to 10% slower on writes.


Fast Set Intersection in Memory

Abstract: Set intersection is a fundamental operation in information retrieval and database systems. This paper introduces linear space data structures to represent sets such that their intersection can be computed in a worst-case efficient way. In general, given k (preprocessed) sets, with totally n elements, we will show how to compute their intersection in expected time O(n/sqrt(w)+kr), where r is the intersection size and w is the number of bits in a machine-word. In addition,we introduce a very simple version of this algorithm that has weaker asymptotic guarantees but performs even better in practice; both algorithms outperform the state of the art techniques in terms of execution time for both synthetic and real data sets and workloads.


Large-Scale Collective Entity Matching

Abstract: There have been several recent advancements in Machine Learning community on the Entity Matching (EM) problem. However, their lack of scalability has prevented them from being applied in practical settings on large real-life datasets. Towards this end, we propose a principled framework to scale any generic EM algorithm. Our technique consists of running multiple instances of the EM algorithm on small neighborhoods of the data and passing messages across neighborhoods to construct a global solution. We prove formal properties of our framework and experimentally demonstrate the effectiveness of our approach in scaling EM algorithms.


Direction-Reversing Quasi-Random Rumor Spreading with Restarts

Abstract: In a recent work, Doerr and Fouz [\emph{Asymptotically Optimal Randomized Rumor Spreading}, in ArXiv] present a new quasi-random PUSH algorithm for the rumor spreading problem (also known as gossip spreading or message propagation problem). Their \emph{hybrid protocol} outperforms all known PUSH protocols. In this work, we add to the hybrid protocol a direction-reversing element. We show that this \emph{direction-reversing quasi-random rumor spreading protocol with random restarts} yields a constant factor improvement over the hybrid model, if we allow the same dose of randomness. Put differently, our protocol achieves the same broadcasting time as the hybrid model by employing only (roughly) half the number of random choices.


Mini-step Strategy for Transient Analysis

Abstract: Domain decomposition methods are widely used to solve sparse linear systems from scientific problems, but they are not suited to solve sparse linear systems extracted from integrated circuits. The reason is that the sparse linear system of integrated circuits may be non-diagonal-dominant, and domain decomposition method might be unconvergent for these non-diagonal-dominant matrices. In this paper, we propose a mini-step strategy to do the circuit transient analysis. Different from the traditional large-step approach, this strategy is able to generate diagonal-dominant sparse linear systems. As a result, preconditioned domain decomposition methods can be used to simulate the large integrated circuits on the supercomputers and clouds.


Enabling Differentiated Services Using Generalized Power Control Model in Optical Networks

Abstract: This paper considers a generalized framework to study OSNR optimization-based end-to-end link level power control problems in optical networks. We combine favorable features of game-theoretical approach and central cost approach to allow different service groups within the network. We develop solutions concepts for both cases of empty and nonempty feasible sets. In addition, we derive and prove the convergence of a distributed iterative algorithm for different classes of users. In the end, we use numerical examples to illustrate the novel framework.


Mechanism design with uncertain inputs (to err is human, to forgive divine)

Abstract: We consider a task of scheduling with a common deadline on a single machine. Every player reports to a scheduler the length of his job and the scheduler needs to finish as many jobs as possible by the deadline. For this simple problem, there is a truthful mechanism that achieves maximum welfare in dominant strategies. The new aspect of our work is that in our setting players are uncertain about their own job lengths, and hence are incapable of providing truthful reports (in the strict sense of the word). For a probabilistic model for uncertainty our main results are as follows. 1) Even with relatively little uncertainty, no mechanism can guarantee a constant fraction of the maximum welfare. 2) To remedy this situation, we introduce a new measure of economic efficiency, based on a notion of a {\em fair share} of a player, and design mechanisms that are $\Omega(1)$-fair. In addition to its intrinsic appeal, our notion of fairness implies good approximation of maximum welfare in several cases of interest. 3) In our mechanisms the machine is sometimes left idle even though there are jobs that want to use it. We show that this unfavorable aspect is unavoidable, unless one gives up other favorable aspects (e.g., give up $\Omega(1)$-fairness). We also consider a qualitative approach to uncertainty as an alternative to the probabilistic quantitative model. In the qualitative approach we break away from solution concepts such as dominant strategies (they are no longer well defined), and instead suggest an axiomatic approach, which amounts to listing desirable properties for mechanisms. We provide a mechanism that satisfies these properties.


Optimal query/update tradeoffs in versioned dictionaries

Abstract: External-memory dictionaries are a fundamental data structure in file systems and databases. Versioned (or fully-persistent) dictionaries have an associated version tree where queries can be performed at any version, updates can be performed on leaf versions, and any version can be `cloned' by adding a child. Various query/update tradeoffs are known for unversioned dictionaries, many of them with matching upper and lower bounds. No fully-versioned external-memory dictionaries are known with optimal space/query/update tradeoffs. In particular, no versioned constructions are known that offer updates in $o(1)$ I/Os using O(N) space. We present the first cache-oblivious and cache-aware constructions that achieve a wide range of optimal points on this tradeoff.


Bounds on the Complexity of Halfspace Intersections when the Bounded Faces have Small Dimension

Abstract: We study the combinatorial complexity of D-dimensional polyhedra defined as the intersection of n halfspaces, with the property that the highest dimension of any bounded face is much smaller than D. We show that, if d is the maximum dimension of a bounded face, then the number of vertices of the polyhedron is O(n^d) and the total number of bounded faces of the polyhedron is O(n^d^2). For inputs in general position the number of bounded faces is O(n^d). For any fixed d, we show how to compute the set of all vertices, how to determine the maximum dimension of a bounded face of the polyhedron, and how to compute the set of bounded faces in polynomial time, by solving a polynomial number of linear programs.


Constant-Time Algorithms for Sparsity Matroids

Abstract: A graph $G=(V,E)$ is called $(k,\ell)$-full if $G$ contains a subgraph $H=(V,F)$ of $k|V|-\ell$ edges such that, for any non-empty $F' \subseteq F$, $|F'| \leq k|V(F')| - \ell$ holds. Here, $V(F')$ denotes the set of vertices incident to $F'$. It is known that the family of edge sets of $(k,\ell)$-full graphs forms a family of matroid, known as the sparsity matroid of $G$. In this paper, we give a constant-time approximation algorithm for the rank of the sparsity matroid of a degree-bounded undirected graph. This leads to a constant-time tester for $(k,\ell)$-fullness in the bounded-degree model, (i.e., we can decide with high probability whether an input graph satisfies a property $P$ or far from $P$). Depending on the values of $k$ and $\ell$, it can test various properties of a graph such as connectivity, rigidity, and how many spanning trees can be packed. Based on this result, we also propose a constant-time tester for $(k,\ell)$-edge-connected-orientability in the bounded-degree model, where an undirected graph $G$ is called $(k,\ell)$-edge-connected-orientable if there exists an orientation $\vec{G}$ of $G$ with a vertex $r \in V$ such that $\vec{G}$ contains $k$ arc-disjoint dipaths from $r$ to each vertex $v \in V$ and $\ell$ arc-disjoint dipaths from each vertex $v \in V$ to $r$. A tester is called a one-sided error tester for $P$ if it always accepts a graph satisfying $P$. We show, for $k \geq 2$ and (proper) $\ell \geq 0$, any one-sided error tester for $(k,\ell)$-fullness and $(k,\ell)$-edge-connected-orientability requires $\Omega(n)$ queries.


Aneka Cloud Application Platform and Its Integration with Windows Azure

Abstract: Aneka is an Application Platform-as-a-Service (Aneka PaaS) for Cloud Computing. It acts as a framework for building customized applications and deploying them on either public or private Clouds. One of the key features of Aneka is its support for provisioning resources on different public Cloud providers such as Amazon EC2, Windows Azure and GoGrid. In this chapter, we will present Aneka platform and its integration with one of the public Cloud infrastructures, Windows Azure, which enables the usage of Windows Azure Compute Service as a resource provider of Aneka PaaS. The integration of the two platforms will allow users to leverage the power of Windows Azure Platform for Aneka Cloud Computing, employing a large number of compute instances to run their applications in parallel. Furthermore, customers of the Windows Azure platform can benefit from the integration with Aneka PaaS by embracing the advanced features of Aneka in terms of multiple programming models, scheduling and management services, application execution services, accounting and pricing services and dynamic provisioning services. Finally, in addition to the Windows Azure Platform we will illustrate in this chapter the integration of Aneka PaaS with other public Cloud platforms such as Amazon EC2 and GoGrid, and virtual machine management platforms such as Xen Server. The new support of provisioning resources on Windows Azure once again proves the adaptability, extensibility and flexibility of Aneka.


Linear pattern matching on sparse suffix trees

Abstract: Packing several characters into one computer word is a simple and natural way to compress the representation of a string and to speed up its processing. Exploiting this idea, we propose an index for a packed string, based on a {\em sparse suffix tree} \cite{KU-96} with appropriately defined suffix links. Assuming, under the standard unit-cost RAM model, that a word can store up to $\log_{\sigma}n$ characters ($\sigma$ the alphabet size), our index takes $O(n/\log_{\sigma}n)$ space, i.e. the same space as the packed string itself. The resulting pattern matching algorithm runs in time $O(m+r^2+r\cdot occ)$, where $m$ is the length of the pattern, $r$ is the actual number of characters stored in a word and $occ$ is the number of pattern occurrences.


Distributed Private Data Analysis: On Simultaneously Solving How and What

Abstract: We examine the combination of two directions in the field of privacy concerning computations over distributed private inputs - secure function evaluation (SFE) and differential privacy. While in both the goal is to privately evaluate some function of the individual inputs, the privacy requirements are significantly different. The general feasibility results for SFE suggest a natural paradigm for implementing differentially private analyses distributively: First choose what to compute, i.e., a differentially private analysis; Then decide how to compute it, i.e., construct an SFE protocol for this analysis. We initiate an examination whether there are advantages to a paradigm where both decisions are made simultaneously. In particular, we investigate under which accuracy requirements it is beneficial to adapt this paradigm for computing a collection of functions including binary sum, gap threshold, and approximate median queries. Our results imply that when computing the binary sum of $n$ distributed inputs then: * When we require that the error is $o(\sqrt{n})$ and the number of rounds is constant, there is no benefit in the new paradigm. * When we allow an error of $O(\sqrt{n})$, the new paradigm yields more efficient protocols when we consider protocols that compute symmetric functions. Our results also yield new separations between the local and global models of computations for private data analysis.


Accelerating Nearest Neighbor Search on Manycore Systems

Abstract: We develop methods for accelerating metric similarity search that are effective on modern hardware. Our algorithms factor into easily parallelizable components, making them simple to deploy and efficient on multicore CPUs and GPUs. Despite the simple structure of our algorithms, their search performance is provably sublinear in the size of the database, with a factor dependent only on its intrinsic dimensionality. We demonstrate that our methods provide substantial speedups on a range of datasets and hardware platforms. In particular, we present results on a 48-core server machine, on graphics hardware, and on a multicore desktop.


Efficient Continual Top-$k$ Keyword Search in Relational Databases

Abstract: Keyword search in relational databases has been widely studied in recent years because it does not require users neither to master a certain structured query language nor to know the complex underlying data schemas. Most of existing methods focus on answering snapshot keyword queries in static databases. In practice, however, databases are updated frequently, and users may have long-term interests on specific topics. To deal with such a situation, it is necessary to build effective and efficient facility in database systems to support continual keyword queries evaluation. In this paper, we propose an efficient method for continual keyword queries answering over relational databases. The proposed method consists of two core algorithms. The first one computes a set of potential top-$k$ results by evaluating the ranges of the future relevance score for every query result and create a light-weight state for each keyword query. The second one uses these states to maintain the top-$k$ results of keyword queries when the database is continually growing. Experimental results validate the effectiveness and efficiency of the proposed method.


Cost Analysis of Redundancy Schemes for Distributed Storage Systems

Abstract: Distributed storage infrastructures require the use of data redundancy to achieve high data reliability. Unfortunately, the use of redundancy introduces storage and communication overheads, which can either reduce the overall storage capacity of the system or increase its costs. To mitigate the storage and communication overhead, different redundancy schemes have been proposed. However, due to the great variety of underlaying storage infrastructures and the different application needs, optimizing these redundancy schemes for each storage infrastructure is cumbersome. The lack of rules to determine the optimal level of redundancy for each storage configuration leads developers in industry to often choose simpler redundancy schemes, which are usually not the optimal ones. In this paper we analyze the cost of different redundancy schemes and derive a set of rules to determine which redundancy scheme minimizes the storage and the communication costs for a given system configuration. Additionally, we use simulation to show that theoretically-optimal schemes may not be viable in a realistic setting where nodes can go off-line and repairs may be delayed. In these cases, we identify which are the trade-offs between the storage and communication overheads of the redundancy scheme and its data reliability.


A Survey of PPAD-Completeness for Computing Nash Equilibria

Abstract: PPAD refers to a class of computational problems for which solutions are guaranteed to exist due to a specific combinatorial principle. The most well-known such problem is that of computing a Nash equilibrium of a game. Other examples include the search for market equilibria, and envy-free allocations in the context of cake-cutting. A problem is said to be complete for PPAD if it belongs to PPAD and can be shown to constitute one of the hardest computational challenges within that class. In this paper, I give a relatively informal overview of the proofs used in the PPAD-completeness results. The focus is on the mixed Nash equilibria guaranteed to exist by Nash's theorem. I also give an overview of some recent work that uses these ideas to show PSPACE-completeness for the computation of specific equilibria found by homotopy methods. I give a brief introduction to related problems of searching for market equilibria.


Memory Retrieval in the B-Matrix Neural Network

Abstract: This paper is an extension to the memory retrieval procedure of the B-Matrix approach [6],[17] to neural network learning. The B-Matrix is a part of the interconnection matrix generated from the Hebbian neural network, and in memory retrieval, the B-matrix is clamped with a small fragment of the memory. The fragment gradually enlarges by means of feedback, until the entire vector is obtained. In this paper, we propose the use of delta learning to enhance the retrieval rate of the stored memories.


Transforming ASN.1 Specifications into CafeOBJ to assist with Property Checking

Abstract: The adoption of algebraic specification/formal method techniques by the networks' research community is happening slowly but steadily. We work towards a software environment that can translate a protocol's specification, from Abstract Syntax Notation One (ASN.1 - a very popular specification language with many applications), into the powerful algebraic specification language CafeOBJ. The resulting code can be used to check, validate and falsify critical properties of systems, at the pre-coding stage of development. In this paper, we introduce some key elements of ASN.1 and CafeOBJ and sketch some first steps towards the implementation of such a tool including a case study.