Column-Oriented Storage Techniques for MapReduce

Abstract: Users of MapReduce often run into performance problems when they scale up their workloads. Many of the problems they encounter can be overcome by applying techniques learned from over three decades of research on parallel DBMSs. However, translating these techniques to a MapReduce implementation such as Hadoop presents unique challenges that can lead to new design choices. This paper describes how column-oriented storage techniques can be incorporated in Hadoop in a way that preserves its popular programming APIs. We show that simply using binary storage formats in Hadoop can provide a 3x performance boost over the naive use of text files. We then introduce a column-oriented storage format that is compatible with the replication and scheduling constraints of Hadoop and show that it can speed up MapReduce jobs on real workloads by an order of magnitude. We also show that dealing with complex column types such as arrays, maps, and nested records, which are common in MapReduce jobs, can incur significant CPU overhead. Finally, we introduce a novel skip list column format and lazy record construction strategy that avoids deserializing unwanted records to provide an additional 1.5x performance boost. Experiments on a real intranet crawl are used to show that our column-oriented storage techniques can improve the performance of the map phase in Hadoop by as much as two orders of magnitude.


Implementing Performance Competitive Logical Recovery

Abstract: New hardware platforms, e.g. cloud, multi-core, etc., have led to a reconsideration of database system architecture. Our Deuteronomy project separates transactional functionality from data management functionality, enabling a flexible response to exploiting new platforms. This separation requires, however, that recovery is described logically. In this paper, we extend current recovery methods to work in this logical setting. While this is straightforward in principle, performance is an issue. We show how ARIES style recovery optimizations can work for logical recovery where page information is not captured on the log. In side-by-side performance experiments using a common log, we compare logical recovery with a state-of-the art ARIES style recovery implementation and show that logical redo performance can be competitive.


Personalized Social Recommendations - Accurate or Private?

Abstract: With the recent surge of social networks like Facebook, new forms of recommendations have become possible - personalized recommendations of ads, content, and even new friend and product connections based on one's social interactions. Since recommendations may use sensitive social information, it is speculated that these recommendations are associated with privacy risks. The main contribution of this work is in formalizing these expected trade-offs between the accuracy and privacy of personalized social recommendations. In this paper, we study whether "social recommendations", or recommendations that are solely based on a user's social network, can be made without disclosing sensitive links in the social graph. More precisely, we quantify the loss in utility when existing recommendation algorithms are modified to satisfy a strong notion of privacy, called differential privacy. We prove lower bounds on the minimum loss in utility for any recommendation algorithm that is differentially private. We adapt two privacy preserving algorithms from the differential privacy literature to the problem of social recommendations, and analyze their performance in comparison to the lower bounds, both analytically and experimentally. We show that good private social recommendations are feasible only for a small subset of the users in the social network or for a lenient setting of privacy parameters.


Efficient Diversification of Web Search Results

Abstract: In this paper we analyze the efficiency of various search results diversification methods. While efficacy of diversification approaches has been deeply investigated in the past, response time and scalability issues have been rarely addressed. A unified framework for studying performance and feasibility of result diversification solutions is thus proposed. First we define a new methodology for detecting when, and how, query results need to be diversified. To this purpose, we rely on the concept of "query refinement" to estimate the probability of a query to be ambiguous. Then, relying on this novel ambiguity detection method, we deploy and compare on a standard test set, three different diversification methods: IASelect, xQuAD, and OptSelect. While the first two are recent state-of-the-art proposals, the latter is an original algorithm introduced in this paper. We evaluate both the efficiency and the effectiveness of our approach against its competitors by using the standard TREC Web diversification track testbed. Results shown that OptSelect is able to run two orders of magnitude faster than the two other state-of-the-art approaches and to obtain comparable figures in diversification effectiveness.


Social content matching in MapReduce

Abstract: Matching problems are ubiquitous. They occur in economic markets, labor markets, internet advertising, and elsewhere. In this paper we focus on an application of matching for social media. Our goal is to distribute content from information suppliers to information consumers. We seek to maximize the overall relevance of the matched content from suppliers to consumers while regulating the overall activity, e.g., ensuring that no consumer is overwhelmed with data and that all suppliers have chances to deliver their content. We propose two matching algorithms, GreedyMR and StackMR, geared for the MapReduce paradigm. Both algorithms have provable approximation guarantees, and in practice they produce high-quality solutions. While both algorithms scale extremely well, we can show that StackMR requires only a poly-logarithmic number of MapReduce steps, making it an attractive option for applications with very large datasets. We experimentally show the trade-offs between quality and efficiency of our solutions on two large datasets coming from real-world social-media web sites.


Physical-Layer Network Coding: Tutorial, Survey, and Beyond

Abstract: The concept of physical-layer network coding (PNC) was proposed in 2006 for application in wireless networks. Since then it has developed into a subfield of network coding with wide followings. The basic idea of PNC is to exploit the network coding operation that occurs naturally when electromagnetic (EM) waves are superimposed on one another. This simple idea turns out to have profound and fundamental ramifications. Subsequent works by various researchers have led to many new results in the domains of 1) wireless communication; 2) wireless information theory; and 3) wireless networking. The purpose of this paper is fourfold. First, we give a brief tutorial on the basic concept of PNC. Second, we survey and discuss recent key results in the three aforementioned areas. Third, we examine a critical issue in PNC: synchronization. It has been a common belief that PNC requires tight synchronization. Our recent results suggest, however, that PNC may actually benefit from asynchrony. Fourth, we propose that PNC is not just for wireless networks; it can also be useful in optical networks. We provide an example showing that the throughput of a passive optical network (PON) could potentially be raised by 100% with PNC.


Calibration with Changing Checking Rules and Its Application to Short-Term Trading

Abstract: We provide a natural learning process in which a financial trader without a risk receives a gain in case when Stock Market is inefficient. In this process, the trader rationally choose his gambles using a prediction made by a randomized calibrated algorithm. Our strategy is based on Dawid's notion of calibration with more general changing checking rules and on some modification of Kakade and Foster's randomized algorithm for computing calibrated forecasts.


A Methodology for Optimizing Multithreaded System Scalability on Multi-cores

Abstract: We show how to quantify scalability with the Universal Scalability Law (USL) by applying it to performance measurements of memcached, J2EE, and Weblogic on multi-core platforms. Since commercial multicores are essentially black-boxes, the accessible performance gains are primarily available at the application level. We also demonstrate how our methodology can identify the most significant performance tuning opportunities to optimize application scalability, as well as providing an easy means for exploring other aspects of the multi-core system design space.


Correction of Noisy Sentences using a Monolingual Corpus

Abstract: Correction of Noisy Natural Language Text is an important and well studied problem in Natural Language Processing. It has a number of applications in domains like Statistical Machine Translation, Second Language Learning and Natural Language Generation. In this work, we consider some statistical techniques for Text Correction. We define the classes of errors commonly found in text and describe algorithms to correct them. The data has been taken from a poorly trained Machine Translation system. The algorithms use only a language model in the target language in order to correct the sentences. We use phrase based correction methods in both the algorithms. The phrases are replaced and combined to give us the final corrected sentence. We also present the methods to model different kinds of errors, in addition to results of the working of the algorithms on the test set. We show that one of the approaches fail to achieve the desired goal, whereas the other succeeds well. In the end, we analyze the possible reasons for such a trend in performance.


Equivalent Effect Function and Fast Intrinsic Mode Decomposition

Abstract: The Equivalent Effect Function (EEF) is defined as having the identical integral values on the control points of the original time series data; the EEF can be obtained from the derivative of the spline function passing through the integral values on the control points. By choosing control points with different criteria, the EEF can be used to find the intrinsic mode function(IMF, fluctuation) and the residue (trend); to fit the curve of the original data function; and to take samples on original data with equivalent effect. As examples of application, results of trend and fluctuation on real stock historical data are calculated on different time scales. A new approach to extend the EEF to 2D intrinsic mode decomposition is introduced to resolve the inter slice non continuity problem, some photo image decomposition examples are presented.


Preprocessing for Automating Early Detection of Cervical Cancer

Abstract: Uterine Cervical Cancer is one of the most common forms of cancer in women worldwide. Most cases of cervical cancer can be prevented through screening programs aimed at detecting precancerous lesions. During Digital Colposcopy, colposcopic images or cervigrams are acquired in raw form. They contain specular reflections which appear as bright spots heavily saturated with white light and occur due to the presence of moisture on the uneven cervix surface and. The cervix region occupies about half of the raw cervigram image. Other parts of the image contain irrelevant information, such as equipment, frames, text and non-cervix tissues. This irrelevant information can confuse automatic identification of the tissues within the cervix. Therefore we focus on the cervical borders, so that we have a geometric boundary on the relevant image area. Our novel technique eliminates the SR, identifies the region of interest and makes the cervigram ready for segmentation algorithms.


Integrating Testing and Interactive Theorem Proving

Abstract: Using an interactive theorem prover to reason about programs involves a sequence of interactions where the user challenges the theorem prover with conjectures. Invariably, many of the conjectures posed are in fact false, and users often spend considerable effort examining the theorem prover's output before realizing this. We present a synergistic integration of testing with theorem proving, implemented in the ACL2 Sedan (ACL2s), for automatically generating concrete counterexamples. Our method uses the full power of the theorem prover and associated libraries to simplify conjectures; this simplification can transform conjectures for which finding counterexamples is hard into conjectures where finding counterexamples is trivial. In fact, our approach even leads to better theorem proving, e.g. if testing shows that a generalization step leads to a false conjecture, we force the theorem prover to backtrack, allowing it to pursue more fruitful options that may yield a proof. The focus of the paper is on the engineering of a synergistic integration of testing with interactive theorem proving; this includes extending ACL2 with new functionality that we expect to be of general interest. We also discuss our experience in using ACL2s to teach freshman students how to reason about their programs.


Default-all is dangerous!

Abstract: We show that the default-all propagation scheme for database annotations is dangerous. Dangerous here means that it can propagate annotations to the query output which are semantically irrelevant to the query the user asked. This is the result of considering all relationally equivalent queries and returning the union of their where-provenance in an attempt to define a propagation scheme that is insensitive to query rewriting. We propose an alternative query-rewrite-insensitive (QRI) where-provenance called minimum propagation. It is analogous to the minimum witness basis for why-provenance, straight-forward to evaluate, and returns all relevant and only relevant annotations.


A Modeling Approach based on UML/MARTE for GPU Architecture

Abstract: Nowadays, the High Performance Computing is part of the context of embedded systems. Graphics Processing Units (GPUs) are more and more used in acceleration of the most part of algorithms and applications. Over the past years, not many efforts have been done to describe abstractions of applications in relation to their target architectures. Thus, when developers need to associate applications and GPUs, for example, they find difficulty and prefer using API for these architectures. This paper presents a metamodel extension for MARTE profile and a model for GPU architectures. The main goal is to specify the task and data allocation in the memory hierarchy of these architectures. The results show that this approach will help to generate code for GPUs based on model transformations using Model Driven Engineering (MDE).


Service Level Agreement for the QoS Guaranteed Mobile IPTV Services over Mobile WiMAX Networks

Abstract: While mobile IPTV services are supported through the mobile WiMAX networks, there must need some guaranteed bandwidth for the IPTV services especially if IPTV and non-IPTV services are simultaneously supported by the mobile WiMAX networks. The quality of an IPTV service definitely depends on the allocated bandwidth for that channel. However, due to the high quality IPTV services and to support of huge non-IPTV traffic over mobile WiMAX networks, it is not possible to guarantee the sufficient amount of the limited mobile WiMAX bandwidth for the mobile IPTV services every time. A Service Level Agreement (SLA) between the mobile IPTV service provider and mobile WiMAX network operator to reserve sufficient bandwidth for the IPTV calls can increase the satisfaction level of the mobile IPTV users. In this paper, we propose a SLA negotiation procedure for mobile IPTV users over mobile WiMAX networks. The Bandwidth Broker controls the allocated bandwidth for IPTV and non-IPTV users. The proposed dynamically reserved bandwidth for the IPTV services increases the IPTV user's satisfaction level. The simulation results state that, our proposed scheme is able to provide better user satisfaction level for the IPTV users.


GutenTag: A Multi-Term Caching Optimized Tag Query Processor for Key-Value Based NoSQL Storage Systems

Abstract: NoSQL systems are more and more deployed as back-end infrastructure for large-scale distributed online platforms like Google, Amazon or Facebook. Their applicability results from the fact that most services of online platforms access the stored data objects via their primary key. However, NoSQL systems do not efficiently support services referring more than one data object, e.g. the term-based search for data objects. To address this issue we propose our architecture based on an inverted index on top of a NoSQL system. For queries comprising more than one term, distributed indices yield a limited performance in large distributed systems. We propose two extensions to cope with this challenge. Firstly, we store index entries not only for single term but also for a selected set of term combinations depending on their popularity derived from a query history. Secondly, we additionally cache popular keys on gateway nodes, which are a common concept in real-world systems, acting as interface for services when accessing data objects in the back end. Our results show that we can significantly reduces the bandwidth consumption for processing queries, with an acceptable, marginal increase in the load of the gateway nodes.


Standardization of information systems development processes and banking industry adaptations

Abstract: This paper examines the current system development processes of three major Turkish banks in terms of compliance to internationally accepted system development and software engineering standards to determine the common process problems of banks. After an in-depth investigation into system development and software engineering standards, related process-based standards were selected. Questions were then prepared covering the whole system development process by applying the classical Waterfall life cycle model. Each question is made up of guidance and suggestions from the international system development standards. To collect data, people from the information technology departments of three major banks in Turkey were interviewed. Results have been aggregated by examining the current process status of the three banks together. Problematic issues were identified using the international system development standards.


On the Cohomology of 3D Digital Images

Abstract: We propose a method for computing the cohomology ring of three--dimensional (3D) digital binary-valued pictures. We obtain the cohomology ring of a 3D digital binary--valued picture $I$, via a simplicial complex K(I)topologically representing (up to isomorphisms of pictures) the picture I. The usefulness of a simplicial description of the "digital" cohomology ring of 3D digital binary-valued pictures is tested by means of a small program visualizing the different steps of the method. Some examples concerning topological thinning, the visualization of representative (co)cycles of (co)homology generators and the computation of the cup product on the cohomology of simple pictures are showed.


A Tool for Integer Homology Computation: Lambda-At Model

Abstract: In this paper, we formalize the notion of lambda-AT-model (where $\lambda$ is a non-null integer) for a given chain complex, which allows the computation of homological information in the integer domain avoiding using the Smith Normal Form of the boundary matrices. We present an algorithm for computing such a model, obtaining Betti numbers, the prime numbers p involved in the invariant factors of the torsion subgroup of homology, the amount of invariant factors that are a power of p and a set of representative cycles of generators of homology mod p, for each p. Moreover, we establish the minimum valid lambda for such a construction, what cuts down the computational costs related to the torsion subgroup. The tools described here are useful to determine topological information of nD structured objects such as simplicial, cubical or simploidal complexes and are applicable to extract such an information from digital pictures.


A Geometric Approach to Matrix Ordering

Abstract: We present a recursive way to partition hypergraphs which creates and exploits hypergraph geometry and is suitable for many-core parallel architectures. Such partitionings are then used to bring sparse matrices in a recursive Bordered Block Diagonal form (for processor-oblivious parallel LU decomposition) or recursive Separated Block Diagonal form (for cache-oblivious sparse matrix-vector multiplication). We show that the quality of the obtained partitionings and orderings is competitive by comparing obtained fill-in for LU decomposition with SuperLU (with better results for 8 of the 28 test matrices) and comparing cut sizes for sparse matrix-vector multiplication with Mondriaan (with better results for 4 of the 12 test matrices). The main advantage of the new method is its speed: it is on average 21.6 times faster than Mondriaan.


Citadel E-Learning: A New Dimension to Learning System

Abstract: E-learning has been an important policy for education planners for many years in developed countries. This policy has been adopted by education in some developing countries; it is therefore expedient to study its emergence in the Nigerian education system. The birth of contemporary technology shows that there is higher requirement for education even in the work force. This has been an eye opener to importance of Education which conveniently can be achieved through E-learning. This work presents CITADEL E-learning approach to Nigeria institutions; its ubiquity, its implementations, its flexibility, portability, ease of use and feature that are synonymous to the standard of education in Nigeria and how it can be enhanced to improve learning for both educators and learners to help them in their learning endeavour.


Deciding Kleene Algebras in Coq

Abstract: We present a reflexive tactic for deciding the equational theory of Kleene algebras in the Coq proof assistant. This tactic relies on a careful implementation of efficient finite automata algorithms, so that it solves casual equations instantaneously and properly scales to larger expressions. The decision procedure is proved correct and complete: correctness is established w.r.t. any model by formalising Kozen's initiality theorem; a counter-example is returned when the given equation does not hold. The correctness proof is challenging: it involves both a precise analysis of the underlying automata algorithms and a lot of algebraic reasoning. In particular, we have to formalise the theory of matrices over a Kleene algebra. We build on the recent addition of firstorder typeclasses in Coq in order to work efficiently with the involved algebraic structures.


Perception of Personality and Naturalness through Dialogues by Native Speakers of American English and Arabic

Abstract: Linguistic markers of personality traits have been studied extensively, but few cross-cultural studies exist. In this paper, we evaluate how native speakers of American English and Arabic perceive personality traits and naturalness of English utterances that vary along the dimensions of verbosity, hedging, lexical and syntactic alignment, and formality. The utterances are the turns within dialogue fragments that are presented as text transcripts to the workers of Amazon's Mechanical Turk. The results of the study suggest that all four dimensions can be used as linguistic markers of all personality traits by both language communities. A further comparative analysis shows cross-cultural differences for some combinations of measures of personality traits and naturalness, the dimensions of linguistic variability and dialogue acts.


Submodular Function Maximization via the Multilinear Relaxation and Contention Resolution Schemes

Abstract: We consider the problem of maximizing a non-negative submodular set function $f:2^N \rightarrow \mathbb{R}_+$ over a ground set $N$ subject to a variety of packing type constraints including (multiple) matroid constraints, knapsack constraints, and their intersections. In this paper we develop a general framework that allows us to derive a number of new results, in particular when $f$ may be a non-monotone function. Our algorithms are based on (approximately) maximizing the multilinear extension $F$ of $f$ over a polytope $P$ that represents the constraints, and then effectively rounding the fractional solution. Although this approach has been used quite successfully, it has been limited in some important ways. We overcome these limitations as follows. First, we give constant factor approximation algorithms to maximize $F$ over a down-closed polytope $P$ described by an efficient separation oracle. Previously this was known only for monotone functions. For non-monotone functions, a constant factor was known only when the polytope was either the intersection of a fixed number of knapsack constraints or a matroid polytope. Second, we show that contention resolution schemes are an effective way to round a fractional solution, even when $f$ is non-monotone. In particular, contention resolution schemes for different polytopes can be combined to handle the intersection of different constraints. Via LP duality we show that a contention resolution scheme for a constraint is related to the correlation gap of weighted rank functions of the constraint. This leads to an optimal contention resolution scheme for the matroid polytope. Our results provide a broadly applicable framework for maximizing linear and submodular functions subject to independence constraints. We give several illustrative examples. Contention resolution schemes may find other applications.


Bounding the Fat Shattering Dimension of a Composition Function Class Built Using a Continuous Logic Connective

Abstract: We begin this report by describing the Probably Approximately Correct (PAC) model for learning a concept class, consisting of subsets of a domain, and a function class, consisting of functions from the domain to the unit interval. Two combinatorial parameters, the Vapnik-Chervonenkis (VC) dimension and its generalization, the Fat Shattering dimension of scale e, are explained and a few examples of their calculations are given with proofs. We then explain Sauer's Lemma, which involves the VC dimension and is used to prove the equivalence of a concept class being distribution-free PAC learnable and it having finite VC dimension. As the main new result of our research, we explore the construction of a new function class, obtained by forming compositions with a continuous logic connective, a uniformly continuous function from the unit hypercube to the unit interval, from a collection of function classes. Vidyasagar had proved that such a composition function class has finite Fat Shattering dimension of all scales if the classes in the original collection do; however, no estimates of the dimension were known. Using results by Mendelson-Vershynin and Talagrand, we bound the Fat Shattering dimension of scale e of this new function class in terms of the Fat Shattering dimensions of the collection's classes. We conclude this report by providing a few open questions and future research topics involving the PAC learning model.


A note on Solving Parametric Polynomial Systems

Abstract: Lazard and Rouillier in [9], by introducing the concept of discriminant variety, have described a new and efficient algorithm for solving parametric polynomial systems. In this paper we modify this algorithm, and we show that with our improvements the output of our algorithm is always minimal and it does not need to compute the radical of ideals.


Online Learning, Stability, and Stochastic Gradient Descent

Abstract: In batch learning, stability together with existence and uniqueness of the solution corresponds to well-posedness of Empirical Risk Minimization (ERM) methods; recently, it was proved that CV_loo stability is necessary and sufficient for generalization and consistency of ERM. In this note, we introduce CV_on stability, which plays a similar note in online learning. We show that stochastic gradient descent (SDG) with the usual hypotheses is CVon stable and we then discuss the implications of CV_on stability for convergence of SGD.


Exploiting Conceptual Knowledge for Querying Information Systems

Abstract: Whereas today's information systems are well-equipped for efficient query handling, their strict mathematical foundations hamper their use for everyday tasks. In daily life, people expect information to be offered in a personalized and focused way. But currently, personalization in digital systems still only takes explicit knowledge into account and does not yet process conceptual information often naturally implied by users. We discuss how to bridge the gap between users and today's systems, building on results from cognitive psychology.


Image Splicing Detection Using Inherent Lens Radial Distortion

Abstract: Image splicing is a common form of image forgery. Such alterations may leave no visual clues of tampering. In recent works camera characteristics consistency across the image has been used to establish the authenticity and integrity of digital images. Such constant camera characteristic properties are inherent from camera manufacturing processes and are unique. The majority of digital cameras are equipped with spherical lens and this introduces radial distortions on images. This aberration is often disturbed and fails to be consistent across the image, when an image is spliced. This paper describes the detection of splicing operation on images by estimating radial distortion from different portions of the image using line-based calibration. For the first time, the detection of image splicing through the verification of consistency of lens radial distortion has been explored in this paper. The conducted experiments demonstrate the efficacy of our proposed approach for the detection of image splicing on both synthetic and real images.


Mapping excellence in the geography of science: An approach based on Scopus data

Abstract: As research becomes an ever more globalized activity, there is growing interest in national and international comparisons of standards and quality in different countries and regions. A sign for this trend is the increasing interest in rankings of universities according to their research performance, both inside but also outside the scientific environment. New methods presented in this paper, enable us to map centers of excellence around the world using programs that are freely available. Based on Scopus data, field-specific excellence can be identified and agglomerated in regions and cities where recently highly-cited papers were published. Differences in performance rates can be visualized on the map using colors and sizes of the marks.


Minimum diameter and cycle-diameter orientations on planar graphs

Abstract: Let G be an edge weighted undirected graph. For every pair of nodes consider the shortest cycle containing these nodes in G. The cycle diameter of G is the maximum length of a cycle in this set. Let H be a directed graph obtained by directing the edges of G. The cycle diameter of H is similarly defined except for that cycles are replaced by directed closed walks. Is there always an orientation H of G whose cycle diameter is bounded by a constant times the cycle diameter of G? We prove this property for planar graphs. These results have implications on the problem of approximating an orientation with minimum diameter


Fault-tolerant Algorithms for Tick-Generation in Asynchronous Logic: Robust Pulse Generation

Abstract: Today's hardware technology presents a new challenge in designing robust systems. Deep submicron VLSI technology introduced transient and permanent faults that were never considered in low-level system designs in the past. Still, robustness of that part of the system is crucial and needs to be guaranteed for any successful product. Distributed systems, on the other hand, have been dealing with similar issues for decades. However, neither the basic abstractions nor the complexity of contemporary fault-tolerant distributed algorithms match the peculiarities of hardware implementations. This paper is intended to be part of an attempt striving to overcome this gap between theory and practice for the clock synchronization problem. Solving this task sufficiently well will allow to build a very robust high-precision clocking system for hardware designs like systems-on-chips in critical applications. As our first building block, we describe and prove correct a novel Byzantine fault-tolerant self-stabilizing pulse synchronization protocol, which can be implemented using standard asynchronous digital logic. Despite the strict limitations introduced by hardware designs, it offers optimal resilience and smaller complexity than all existing protocols.


Search for Hidden Knowledge in Collective Intelligence dealing Indeterminacy Ontology of Folksonomy with Linguistic Pragmatics and Quantum Logic

Abstract: Information retrieval is not only the most frequent application executed on the Web but it is also the base of different types of applications. Considering collective intelligence of groups of individuals as a framework for evaluating and incorporating new experiences and information we often cannot retrieve such knowledge being tacit. Tacit knowledge underlies many competitive capabilities and it is hard to articulate on discrete ontology structure. It is unstructured or unorganized, and therefore remains hidden. Developing generic solutions that can find the hidden knowledge is extremely complex. Moreover this will be a great challenge for the developers of semantic technologies. This work aims to explore ways to make explicit and available the tacit knowledge hidden in the collective intelligence of a collaborative environment within organizations. The environment was defined by folksonomies supported by a faceted semantic search. Vector space model which incorporates an analogy with the mathematical apparatus of quantum theory is adopted for the representation and manipulation of the meaning of folksonomy. Vector space retrieval has been proven efficiency when there isn't a data behavioural because it bears ranking algorithms involving a small number of types of elements and few operations. A solution to find what the user has in mind when posing a query could be based on "joint meaning" understood as a joint construal of the creator of the contents and the reader of the contents. The joint meaning was proposed to deal with vagueness on ontology of folksonomy indeterminacy, incompleteness and inconsistencies on collective intelligence. A proof-of concept prototype was built for collaborative environment as evolution of the actual social networks (like Facebook, LinkedIn,..) using the information visualization on a RIA application with Semantic Web techniques and technologies.


A Knowledge base model for complex forging die machining

Abstract: Recent evolutions on forging process induce more complex shape on forging die. These evolutions, combined with High Speed Machining (HSM) process of forging die lead to important increase in time for machining preparation. In this context, an original approach for generating machining process based on machining knowledge is proposed in this paper. The core of this approach is to decompose a CAD model of complex forging die in geometric features. Technological data and topological relations are aggregated to a geometric feature in order to create machining features. Technological data, such as material, surface roughness and form tolerance are defined during forging process and dies design. These data are used to choose cutting tools and machining strategies. Topological relations define relative positions between the surfaces of the die CAD model. After machining features identification cutting tools and machining strategies currently used in HSM of forging die, are associated to them in order to generate machining sequences. A machining process model is proposed to formalize the links between information imbedded in the machining features and the parameters of cutting tools and machining strategies. At last machining sequences are grouped and ordered to generate the complete die machining process. In this paper the identification of geometrical features is detailed. Geometrical features identification is based on machining knowledge formalization which is translated in the generation of maps from STL models. A map based on the contact area between cutting tools and die shape gives basic geometrical features which are connected or not according to the continuity maps. The proposed approach is illustrated by an application on an industrial study case which was accomplished as part of collaboration.


Distributed Evolutionary Computation using REST

Abstract: This paper analises distributed evolutionary computation based on the Representational State Transfer (REST) protocol, which overlays a farming model on evolutionary computation. An approach to evolutionary distributed optimisation of multilayer perceptrons (MLP) using REST and language Perl has been done. In these experiments, a master-slave based evolutionary algorithm (EA) has been implemented, where slave processes evaluate the costly fitness function (training a MLP to solve a classification problem). Obtained results show that the parallel version of the developed programs obtains similar or better results using much less time than the sequential version, obtaining a good speedup.


SOAP vs REST: Comparing a master-slave GA implementation

Abstract: In this paper, a high-level comparison of both SOAP (Simple Object Access Protocol) and REST (Representational State Transfer) is made. These are the two main approaches for interfacing to the web with web services. Both approaches are different and present some advantages and disadvantages for interfacing to web services: SOAP is conceptually more difficult (has a steeper learning curve) and more "heavy-weight" than REST, although it lacks of standards support for security. In order to test their eficiency (in time), two experiments have been performed using both technologies: a client-server model implementation and a master-slave based genetic algorithm (GA). The results obtained show clear differences in time between SOAP and REST implementations. Although both techniques are suitable for developing parallel systems, SOAP is heavier than REST, mainly due to the verbosity of SOAP communications (XML increases the time taken to parse the messages).


The Complexity of Manipulative Attacks in Nearly Single-Peaked Electorates

Abstract: Many electoral bribery, control, and manipulation problems (which we will refer to in general as "manipulative actions" problems) are NP-hard in the general case. It has recently been noted that many of these problems fall into polynomial time if the electorate is single-peaked (i.e., is polarized along some axis/issue). However, real-world electorates are not truly single-peaked. There are usually some mavericks, and so real-world electorates tend to merely be nearly single-peaked. This paper studies the complexity of manipulative-action algorithms for elections over nearly single-peaked electorates, for various notions of nearness and various election systems. We provide instances where even one maverick jumps the manipulative-action complexity up to $\np$-hardness, but we also provide many instances where a reasonable number of mavericks can be tolerated without increasing the manipulative-action complexity.


A faster exact multiprocessor schedulability test for sporadic tasks

Abstract: Baker and Cirinei introduced an exact but naive algorithm, based on solving a state reachability problem in a finite automaton, to check whether sets of sporadic hard real-time tasks are schedulable on identical multiprocessor platforms. However, the algorithm suffered from poor performance due to the exponential size of the automaton relative to the size of the task set. In this paper, we successfully apply techniques developed by the formal verification community, specifically antichain algorithms, by defining and proving the correctness of a simulation relation on Baker and Cirinei's automaton. We show our improved algorithm yields dramatically improved performance for the schedulability test and opens for many further improvements.


Reserved or On-Demand Instances? A Revenue Maximization Model for Cloud Providers

Abstract: We examine the problem of managing a server farm in a way that attempts to maximize the net revenue earned by a cloud provider by renting servers to customers according to a typical Platform-as-a-Service model. The Cloud provider offers its resources to two classes of customers: `premium' and `basic'. Premium customers pay upfront fees to reserve servers for a specified period of time (e.g. a year). Premium customers can submit jobs for their reserved servers at any time and pay a fee for the server-hours they use. The provider is liable to pay a penalty every time a `premium' job can not be executed due to lack of resources. On the other hand, `basic' customers are served on a best-effort basis, and pay a server-hour fee that may be higher than the one paid by premium customers. The provider incurs energy costs when running servers. Hence, it has an incentive to turn off idle servers. The question of how to choose the number of servers to allocate to each pool (basic and premium) is answered by analyzing a suitable queuing model and maximizing a revenue function. Experimental results show that the proposed scheme adapts to different traffic conditions, penalty levels, energy costs and usage fees.


Scheduling of Hard Real-Time Multi-Thread Periodic Tasks

Abstract: In this paper we study the scheduling of parallel and real-time recurrent tasks. Firstly, we propose a new parallel task model which allows recurrent tasks to be composed of several threads, each thread requires a single processor for execution and can be scheduled simultaneously. Secondly, we define several kinds of real-time schedulers that can be applied to our parallel task model. We distinguish between two scheduling classes: hierarchical schedulers and global thread schedulers. We present and prove correct an exact schedulability test for each class. Lastly, we also evaluate the performance of our scheduling paradigm in comparison with Gang scheduling by means of simulations.


Scheduling under Precedence, Communication, and Energy Constraints

Abstract: We consider the problem of scheduling a set of $n$ tasks on $m$ processors under precedence, communication, and global system energy constraints to minimize makespan. We extend existing scheduling models to account for energy usage and give convex programming algorithms that yield essentially the same results as existing algorithms that do not consider energy, while adhering to a strict energy bound.


Large-Scale Music Annotation and Retrieval: Learning to Rank in Joint Semantic Spaces

Abstract: Music prediction tasks range from predicting tags given a song or clip of audio, predicting the name of the artist, or predicting related songs given a song, clip, artist name or tag. That is, we are interested in every semantic relationship between the different musical concepts in our database. In realistically sized databases, the number of songs is measured in the hundreds of thousands or more, and the number of artists in the tens of thousands or more, providing a considerable challenge to standard machine learning techniques. In this work, we propose a method that scales to such datasets which attempts to capture the semantic similarities between the database items by modeling audio, artist names, and tags in a single low-dimensional semantic space. This choice of space is learnt by optimizing the set of prediction tasks of interest jointly using multi-task learning. Our method both outperforms baseline methods and, in comparison to them, is faster and consumes less memory. We then demonstrate how our method learns an interpretable model, where the semantic space captures well the similarities of interest.


Misleading Stars: What Cannot Be Measured in the Internet?

Abstract: Traceroute measurements are one of our main instruments to shed light onto the structure and properties of today's complex networks such as the Internet. This paper studies the feasibility and infeasibility of inferring the network topology given traceroute data from a worst-case perspective, i.e., without any probabilistic assumptions on, e.g., the nodes' degree distribution. We attend to a scenario where some of the routers are anonymous, and propose two fundamental axioms that model two basic assumptions on the traceroute data: (1) each trace corresponds to a real path in the network, and (2) the routing paths are at most a factor 1/alpha off the shortest paths, for some parameter alpha in (0,1]. In contrast to existing literature that focuses on the cardinality of the set of (often only minimal) inferrable topologies, we argue that a large number of possible topologies alone is often unproblematic, as long as the networks have a similar structure. We hence seek to characterize the set of topologies inferred with our axioms. We introduce the notion of star graphs whose colorings capture the differences among inferred topologies; it also allows us to construct inferred topologies explicitly. We find that in general, inferrable topologies can differ significantly in many important aspects, such as the nodes' distances or the number of triangles. These negative results are complemented by a discussion of a scenario where the trace set is best possible, i.e., "complete". It turns out that while some properties such as the node degrees are still hard to measure, a complete trace set can help to determine global properties such as the connectivity.


Composition Games for Distributed Systems: the EU Grant games

Abstract: We analyze ways by which people decompose into groups in distributed systems. We are interested in systems in which an agent can increase its utility by connecting to other agents, but must also pay a cost that increases with the size of the sys- tem. The right balance is achieved by the right size group of agents. We formulate and analyze three intuitive and realistic games and show how simple changes in the protocol can dras- tically improve the price of anarchy of these games. In partic- ular, we identify two important properties for a low price of anarchy: agreement in joining the system, and the possibil- ity of appealing a rejection from a system. We show that the latter property is especially important if there are some pre- existing constraints regarding who may collaborate (or com- municate) with whom.


Relaxing Tight Frame Condition in Parallel Proximal Methods for Signal Restoration

Abstract: A fruitful approach for solving signal deconvolution problems consists of resorting to a frame-based convex variational formulation. In this context, parallel proximal algorithms and related alternating direction methods of multipliers have become popular optimization techniques to approximate iteratively the desired solution. Until now, in most of these methods, either Lipschitz differentiability properties or tight frame representations were assumed. In this paper, it is shown that it is possible to relax these assumptions by considering a class of non necessarily tight frame representations, thus offering the possibility of addressing a broader class of signal restoration problems. In particular, it is possible to use non necessarily maximally decimated filter banks with perfect reconstruction, which are common tools in digital signal processing. The proposed approach allows us to solve both frame analysis and frame synthesis problems for various noise distributions. In our simulations, it is applied to the deconvolution of data corrupted with Poisson noise or Laplacian noise by using (non-tight) discrete dual-tree wavelet representations and filter bank structures.


State Space Reduction in the Maude-NRL Protocol Analyzer

Abstract: The Maude-NRL Protocol Analyzer (Maude-NPA) is a tool and inference system for reasoning about the security of cryptographic protocols in which the cryptosystems satisfy different equational properties. It both extends and provides a formal framework for the original NRL Protocol Analyzer, which supported equational reasoning in a more limited way. Maude-NPA supports a wide variety of algebraic properties that includes many crypto-systems of interest such as, for example, one-time pads and Diffie-Hellman. Maude-NPA, like the original NPA, looks for attacks by searching backwards from an insecure attack state, and assumes an unbounded number of sessions. Because of the unbounded number of sessions and the support for different equational theories, it is necessary to develop ways of reducing the search space and avoiding infinite search paths. In order for the techniques to prove useful, they need not only to speed up the search, but should not violate completeness, so that failure to find attacks still guarantees security. In this paper we describe some state space reduction techniques that we have implemented in Maude-NPA. We also provide completeness proofs, and experimental evaluations of their effect on the performance of Maude-NPA.


Efficient Learning of Sparse Invariant Representations

Abstract: We propose a simple and efficient algorithm for learning sparse invariant representations from unlabeled data with fast inference. When trained on short movies sequences, the learned features are selective to a range of orientations and spatial frequencies, but robust to a wide range of positions, similar to complex cells in the primary visual cortex. We give a hierarchical version of the algorithm, and give guarantees of fast convergence under certain conditions.


Common Edge-Unzippings for Tetrahedra

Abstract: It is shown that there are examples of distinct polyhedra, each with a Hamiltonian path of edges, which when cut, unfolds the surfaces to a common net. In particular, it is established for infinite classes of triples of tetrahedra.


The Ariadne's Clew Algorithm

Abstract: We present a new approach to path planning, called the "Ariadne's clew algorithm". It is designed to find paths in high-dimensional continuous spaces and applies to robots with many degrees of freedom in static, as well as dynamic environments - ones where obstacles may move. The Ariadne's clew algorithm comprises two sub-algorithms, called Search and Explore, applied in an interleaved manner. Explore builds a representation of the accessible space while Search looks for the target. Both are posed as optimization problems. We describe a real implementation of the algorithm to plan paths for a six degrees of freedom arm in a dynamic environment where another six degrees of freedom arm is used as a moving obstacle. Experimental results show that a path is found in about one second without any pre-processing.


Computational Aspects of Reordering Plans

Abstract: This article studies the problem of modifying the action ordering of a plan in order to optimise the plan according to various criteria. One of these criteria is to make a plan less constrained and the other is to minimize its parallel execution time. Three candidate definitions are proposed for the first of these criteria, constituting a sequence of increasing optimality guarantees. Two of these are based on deordering plans, which means that ordering relations may only be removed, not added, while the third one uses reordering, where arbitrary modifications to the ordering are allowed. It is shown that only the weakest one of the three criteria is tractable to achieve, the other two being NP-hard and even difficult to approximate. Similarly, optimising the parallel execution time of a plan is studied both for deordering and reordering of plans. In the general case, both of these computations are NP-hard. However, it is shown that optimal deorderings can be computed in polynomial time for a class of planning languages based on the notions of producers, consumers and threats, which includes most of the commonly used planning languages. Computing optimal reorderings can potentially lead to even faster parallel executions, but this problem remains NP-hard and difficult to approximate even under quite severe restrictions.


The Divide-and-Conquer Subgoal-Ordering Algorithm for Speeding up Logic Inference

Abstract: It is common to view programs as a combination of logic and control: the logic part defines what the program must do, the control part -- how to do it. The Logic Programming paradigm was developed with the intention of separating the logic from the control. Recently, extensive research has been conducted on automatic generation of control for logic programs. Only a few of these works considered the issue of automatic generation of control for improving the efficiency of logic programs. In this paper we present a novel algorithm for automatic finding of lowest-cost subgoal orderings. The algorithm works using the divide-and-conquer strategy. The given set of subgoals is partitioned into smaller sets, based on co-occurrence of free variables. The subsets are ordered recursively and merged, yielding a provably optimal order. We experimentally demonstrate the utility of the algorithm by testing it in several domains, and discuss the possibilities of its cooperation with other existing methods.


The Gn,m Phase Transition is Not Hard for the Hamiltonian Cycle Problem

Abstract: Using an improved backtrack algorithm with sophisticated pruning techniques, we revise previous observations correlating a high frequency of hard to solve Hamiltonian Cycle instances with the Gn,m phase transition between Hamiltonicity and non-Hamiltonicity. Instead all tested graphs of 100 to 1500 vertices are easily solved. When we artificially restrict the degree sequence with a bounded maximum degree, although there is some increase in difficulty, the frequency of hard graphs is still low. When we consider more regular graphs based on a generalization of knight's tours, we observe frequent instances of really hard graphs, but on these the average degree is bounded by a constant. We design a set of graphs with a feature our algorithm is unable to detect and so are very hard for our algorithm, but in these we can vary the average degree from O(1) to O(n). We have so far found no class of graphs correlated with the Gn,m phase transition which asymptotically produces a high frequency of hard instances.


Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language

Abstract: This article presents a measure of semantic similarity in an IS-A taxonomy based on the notion of shared information content. Experimental evaluation against a benchmark set of human similarity judgments demonstrates that the measure performs better than the traditional edge-counting approach. The article presents algorithms that take advantage of taxonomic similarity in resolving syntactic and semantic ambiguity, along with experimental results demonstrating their effectiveness.


A Temporal Description Logic for Reasoning about Actions and Plans

Abstract: A class of interval-based temporal languages for uniformly representing and reasoning about actions and plans is presented. Actions are represented by describing what is true while the action itself is occurring, and plans are constructed by temporally relating actions and world states. The temporal languages are members of the family of Description Logics, which are characterized by high expressivity combined with good computational properties. The subsumption problem for a class of temporal Description Logics is investigated and sound and complete decision procedures are given. The basic language TL-F is considered first: it is the composition of a temporal logic TL -- able to express interval temporal networks -- together with the non-temporal logic F -- a Feature Description Logic. It is proven that subsumption in this language is an NP-complete problem. Then it is shown how to reason with the more expressive languages TLU-FU and TL-ALCF. The former adds disjunction both at the temporal and non-temporal sides of the language, the latter extends the non-temporal side with set-valued features (i.e., roles) and a propositionally complete language.


Adaptive Parallel Iterative Deepening Search

Abstract: Many of the artificial intelligence techniques developed to date rely on heuristic search through large spaces. Unfortunately, the size of these spaces and the corresponding computational effort reduce the applicability of otherwise novel and effective algorithms. A number of parallel and distributed approaches to search have considerably improved the performance of the search process. Our goal is to develop an architecture that automatically selects parallel search strategies for optimal performance on a variety of search problems. In this paper we describe one such architecture realized in the Eureka system, which combines the benefits of many different approaches to parallel heuristic search. Through empirical and theoretical analyses we observe that features of the problem space directly affect the choice of optimal parallel search strategy. We then employ machine learning techniques to select the optimal parallel search strategy for a given problem space. When a new search task is input to the system, Eureka uses features describing the search space and the chosen architecture to automatically select the appropriate search strategy. Eureka has been tested on a MIMD parallel processor, a distributed network of workstations, and a single workstation using multithreading. Results generated from fifteen puzzle problems, robot arm motion problems, artificial search spaces, and planning problems indicate that Eureka outperforms any of the tested strategies used exclusively for all problem instances and is able to greatly reduce the search time for these applications.


Order of Magnitude Comparisons of Distance

Abstract: Order of magnitude reasoning - reasoning by rough comparisons of the sizes of quantities - is often called 'back of the envelope calculation', with the implication that the calculations are quick though approximate. This paper exhibits an interesting class of constraint sets in which order of magnitude reasoning is demonstrably fast. Specifically, we present a polynomial-time algorithm that can solve a set of constraints of the form 'Points a and b are much closer together than points c and d.' We prove that this algorithm can be applied if `much closer together' is interpreted either as referring to an infinite difference in scale or as referring to a finite difference in scale, as long as the difference in scale is greater than the number of variables in the constraint set. We also prove that the first-order theory over such constraints is decidable.


AntNet: Distributed Stigmergetic Control for Communications Networks

Abstract: This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.


A Counter Example to Theorems of Cox and Fine

Abstract: Cox's well-known theorem justifying the use of probability is shown not to hold in finite domains. The counterexample also suggests that Cox's assumptions are insufficient to prove the result even in infinite domains. The same counterexample is used to disprove a result of Fine on comparative conditional probability.


The Automatic Inference of State Invariants in TIM

Abstract: As planning is applied to larger and richer domains the effort involved in constructing domain descriptions increases and becomes a significant burden on the human application designer. If general planners are to be applied successfully to large and complex domains it is necessary to provide the domain designer with some assistance in building correctly encoded domains. One way of doing this is to provide domain-independent techniques for extracting, from a domain description, knowledge that is implicit in that description and that can assist domain designers in debugging domain descriptions. This knowledge can also be exploited to improve the performance of planners: several researchers have explored the potential of state invariants in speeding up the performance of domain-independent planners. In this paper we describe a process by which state invariants can be extracted from the automatically inferred type structure of a domain. These techniques are being developed for exploitation by STAN, a Graphplan based planner that employs state analysis techniques to enhance its performance.


Unifying Class-Based Representation Formalisms

Abstract: The notion of class is ubiquitous in computer science and is central in many formalisms for the representation of structured knowledge used both in knowledge representation and in databases. In this paper we study the basic issues underlying such representation formalisms and single out both their common characteristics and their distinguishing features. Such investigation leads us to propose a unifying framework in which we are able to capture the fundamental aspects of several representation languages used in different contexts. The proposed formalism is expressed in the style of description logics, which have been introduced in knowledge representation as a means to provide a semantically well-founded basis for the structural aspects of knowledge representation systems. The description logic considered in this paper is a subset of first order logic with nice computational characteristics. It is quite expressive and features a novel combination of constructs that has not been studied before. The distinguishing constructs are number restrictions, which generalize existence and functional dependencies, inverse roles, which allow one to refer to the inverse of a relationship, and possibly cyclic assertions, which are necessary for capturing real world domains. We are able to show that it is precisely such combination of constructs that makes our logic powerful enough to model the essential set of features for defining class structures that are common to frame systems, object-oriented database languages, and semantic data models. As a consequence of the established correspondences, several significant extensions of each of the above formalisms become available. The high expressiveness of the logic we propose and the need for capturing the reasoning in different contexts forces us to distinguish between unrestricted and finite model reasoning. A notable feature of our proposal is that reasoning in both cases is decidable. We argue that, by virtue of the high expressive power and of the associated reasoning capabilities on both unrestricted and finite models, our logic provides a common core for class-based representation formalisms.


Complexity of Prioritized Default Logics

Abstract: In default reasoning, usually not all possible ways of resolving conflicts between default rules are acceptable. Criteria expressing acceptable ways of resolving the conflicts may be hardwired in the inference mechanism, for example specificity in inheritance reasoning can be handled this way, or they may be given abstractly as an ordering on the default rules. In this article we investigate formalizations of the latter approach in Reiter's default logic. Our goal is to analyze and compare the computational properties of three such formalizations in terms of their computational complexity: the prioritized default logics of Baader and Hollunder, and Brewka, and a prioritized default logic that is based on lexicographic comparison. The analysis locates the propositional variants of these logics on the second and third levels of the polynomial hierarchy, and identifies the boundary between tractable and intractable inference for restricted classes of prioritized default theories.


Squeaky Wheel Optimization

Abstract: We describe a general approach to optimization which we term `Squeaky Wheel' Optimization (SWO). In SWO, a greedy algorithm is used to construct a solution which is then analyzed to find the trouble spots, i.e., those elements, that, if improved, are likely to improve the objective function score. The results of the analysis are used to generate new priorities that determine the order in which the greedy algorithm constructs the next solution. This Construct/Analyze/Prioritize cycle continues until some limit is reached, or an acceptable solution is found. SWO can be viewed as operating on two search spaces: solutions and prioritizations. Successive solutions are only indirectly related, via the re-prioritization that results from analyzing the prior solution. Similarly, successive prioritizations are generated by constructing and analyzing solutions. This `coupled search' has some interesting properties, which we discuss. We report encouraging experimental results on two domains, scheduling problems that arise in fiber-optic cable manufacturing, and graph coloring problems. The fact that these domains are very different supports our claim that SWO is a general technique for optimization.


Variational Cumulant Expansions for Intractable Distributions

Abstract: Intractable distributions present a common difficulty in inference within the probabilistic knowledge representation framework and variational methods have recently been popular in providing an approximate solution. In this article, we describe a perturbational approach in the form of a cumulant expansion which, to lowest order, recovers the standard Kullback-Leibler variational bound. Higher-order terms describe corrections on the variational approach without incurring much further computational cost. The relationship to other perturbational approaches such as TAP is also elucidated. We demonstrate the method on a particular class of undirected graphical models, Boltzmann machines, for which our simulation results confirm improved accuracy and enhanced stability during learning.


Efficient Implementation of the Plan Graph in STAN

Abstract: STAN is a Graphplan-based planner, so-called because it uses a variety of STate ANalysis techniques to enhance its performance. STAN competed in the AIPS-98 planning competition where it compared well with the other competitors in terms of speed, finding solutions fastest to many of the problems posed. Although the domain analysis techniques STAN exploits are an important factor in its overall performance, we believe that the speed at which STAN solved the competition problems is largely due to the implementation of its plan graph. The implementation is based on two insights: that many of the graph construction operations can be implemented as bit-level logical operations on bit vectors, and that the graph should not be explicitly constructed beyond the fix point. This paper describes the implementation of STAN's plan graph and provides experimental results which demonstrate the circumstances under which advantages can be obtained from using this implementation.


Cooperation between Top-Down and Bottom-Up Theorem Provers

Abstract: Top-down and bottom-up theorem proving approaches each have specific advantages and disadvantages. Bottom-up provers profit from strong redundancy control but suffer from the lack of goal-orientation, whereas top-down provers are goal-oriented but often have weak calculi when their proof lengths are considered. In order to integrate both approaches, we try to achieve cooperation between a top-down and a bottom-up prover in two different ways: The first technique aims at supporting a bottom-up with a top-down prover. A top-down prover generates subgoal clauses, they are then processed by a bottom-up prover. The second technique deals with the use of bottom-up generated lemmas in a top-down prover. We apply our concept to the areas of model elimination and superposition. We discuss the ability of our techniques to shorten proofs as well as to reorder the search space in an appropriate manner. Furthermore, in order to identify subgoal clauses and lemmas which are actually relevant for the proof task, we develop methods for a relevancy-based filtering. Experiments with the provers SETHEO and SPASS performed in the problem library TPTP reveal the high potential of our cooperation approaches.


Solving Highly Constrained Search Problems with Quantum Computers

Abstract: A previously developed quantum search algorithm for solving 1-SAT problems in a single step is generalized to apply to a range of highly constrained k-SAT problems. We identify a bound on the number of clauses in satisfiability problems for which the generalized algorithm can find a solution in a constant number of steps as the number of variables increases. This performance contrasts with the linear growth in the number of steps required by the best classical algorithms, and the exponential number required by classical and quantum methods that ignore the problem structure. In some cases, the algorithm can also guarantee that insoluble problems in fact have no solutions, unlike previously proposed quantum search algorithms.


Decision-Theoretic Planning: Structural Assumptions and Computational Leverage

Abstract: Planning under uncertainty is a central problem in the study of automated sequential decision making, and has been addressed by researchers in many different fields, including AI planning, decision analysis, operations research, control theory and economics. While the assumptions and perspectives adopted in these areas often differ in substantial ways, many planning problems of interest to researchers in these fields can be modeled as Markov decision processes (MDPs) and analyzed using the techniques of decision theory. This paper presents an overview and synthesis of MDP-related methods, showing how they provide a unifying framework for modeling many classes of planning problems studied in AI. It also describes structural properties of MDPs that, when exhibited by particular classes of problems, can be exploited in the construction of optimal or approximately optimal policies or plans. Planning problems commonly possess structure in the reward and value functions used to describe performance criteria, in the functions used to describe state transitions and observations, and in the relationships among features used to describe states, actions, rewards, and observations. Specialized representations, and algorithms employing these representations, can achieve computational leverage by exploiting these various forms of structure. Certain AI techniques -- in particular those based on the use of structured, intensional representations -- can be viewed in this way. This paper surveys several types of representations for both classical and decision-theoretic planning problems, and planning algorithms that exploit these representations in a number of different ways to ease the computational burden of constructing policies or plans. It focuses primarily on abstraction, aggregation and decomposition techniques based on AI-style representations.


Probabilistic Deduction with Conditional Constraints over Basic Events

Abstract: We study the problem of probabilistic deduction with conditional constraints over basic events. We show that globally complete probabilistic deduction with conditional constraints over basic events is NP-hard. We then concentrate on the special case of probabilistic deduction in conditional constraint trees. We elaborate very efficient techniques for globally complete probabilistic deduction. In detail, for conditional constraint trees with point probabilities, we present a local approach to globally complete probabilistic deduction, which runs in linear time in the size of the conditional constraint trees. For conditional constraint trees with interval probabilities, we show that globally complete probabilistic deduction can be done in a global approach by solving nonlinear programs. We show how these nonlinear programs can be transformed into equivalent linear programs, which are solvable in polynomial time in the size of the conditional constraint trees.


Variational Probabilistic Inference and the QMR-DT Network

Abstract: We describe a variational approximation method for efficient inference in large-scale probabilistic models. Variational methods are deterministic procedures that provide approximations to marginal and conditional probabilities of interest. They provide alternatives to approximate inference methods based on stochastic sampling or search. We describe a variational approach to the problem of diagnostic inference in the `Quick Medical Reference' (QMR) network. The QMR network is a large-scale probabilistic graphical model built on statistical and expert knowledge. Exact probabilistic inference is infeasible in this model for all but a small set of cases. We evaluate our variational inference algorithm on a large set of diagnostic test cases, comparing the algorithm to a state-of-the-art stochastic sampling method.


Extensible Knowledge Representation: the Case of Description Reasoners

Abstract: This paper offers an approach to extensible knowledge representation and reasoning for a family of formalisms known as Description Logics. The approach is based on the notion of adding new concept constructors, and includes a heuristic methodology for specifying the desired extensions, as well as a modularized software architecture that supports implementing extensions. The architecture detailed here falls in the normalize-compared paradigm, and supports both intentional reasoning (subsumption) involving concepts, and extensional reasoning involving individuals after incremental updates to the knowledge base. The resulting approach can be used to extend the reasoner with specialized notions that are motivated by specific problems or application areas, such as reasoning about dates, plans, etc. In addition, it provides an opportunity to implement constructors that are not currently yet sufficiently well understood theoretically, but are needed in practice. Also, for constructors that are provably hard to reason with (e.g., ones whose presence would lead to undecidability), it allows the implementation of incomplete reasoners where the incompleteness is tailored to be acceptable for the application at hand.


Learning to Order Things

Abstract: There are many applications in which it is desirable to order rather than classify instances. Here we consider the problem of learning how to order instances given feedback in the form of preference judgments, i.e., statements to the effect that one instance should be ranked ahead of another. We outline a two-stage approach in which one first learns by conventional means a binary preference function indicating whether it is advisable to rank one instance before another. Here we consider an on-line algorithm for learning preference functions that is based on Freund and Schapire's 'Hedge' algorithm. In the second stage, new instances are ordered so as to maximize agreement with the learned preference function. We show that the problem of finding the ordering that agrees best with a learned preference function is NP-complete. Nevertheless, we describe simple greedy algorithms that are guaranteed to find a good approximation. Finally, we show how metasearch can be formulated as an ordering problem, and present experimental results on learning a combination of 'search experts', each of which is a domain-specific query expansion strategy for a web search engine.


Constructing Conditional Plans by a Theorem-Prover

Abstract: The research on conditional planning rejects the assumptions that there is no uncertainty or incompleteness of knowledge with respect to the state and changes of the system the plans operate on. Without these assumptions the sequences of operations that achieve the goals depend on the initial state and the outcomes of nondeterministic changes in the system. This setting raises the questions of how to represent the plans and how to perform plan search. The answers are quite different from those in the simpler classical framework. In this paper, we approach conditional planning from a new viewpoint that is motivated by the use of satisfiability algorithms in classical planning. Translating conditional planning to formulae in the propositional logic is not feasible because of inherent computational limitations. Instead, we translate conditional planning to quantified Boolean formulae. We discuss three formalizations of conditional planning as quantified Boolean formulae, and present experimental results obtained with a theorem-prover.


Issues in Stacked Generalization

Abstract: Stacked generalization is a general method of using a high-level model to combine lower-level models to achieve greater predictive accuracy. In this paper we address two crucial issues which have been considered to be a `black art' in classification tasks ever since the introduction of stacked generalization in 1992 by Wolpert: the type of generalizer that is suitable to derive the higher-level model, and the kind of attributes that should be used as its input. We find that best results are obtained when the higher-level model combines the confidence (and not just the predictions) of the lower-level ones. We demonstrate the effectiveness of stacked generalization for combining three different types of learning algorithms for classification tasks. We also compare the performance of stacked generalization with majority vote and published results of arcing and bagging.


Smoothing and Modeling of Video Transmission Rates over a QoS Network with Limited Bandwidth Connections

Abstract: Transmission of video over a limited bandwidth network is challenging due to the natural variability of video, and link characteristics. Video smoothing techniques can be used to facilitate more effective transmission and to preserve better quality. In this paper we develop a semi-optimal video smoothing approach to manage the transmission of MPEG-4 and H.264 video while mapping it to be more suitable for a QoS based network. The proposed technique utilizes a smoothing buffer with pre-defined thresholds to smooth the transmission rates while assuming minimal information about the video to be transmitted. The results obtained showed a significant improvements in transmission rate variability while guaranteeing no buffer overflows or underflows. In addition, a queuing model is developed for the used smoothing algorithm for H.264 video streams with optimized encoding and packetization, utilizing the available H.264 macroblock ordering option.


Ontology Alignment at the Instance and Schema Level

Abstract: We present PARIS, an approach for the automatic alignment of ontologies. PARIS aligns not only instances, but also relations and classes. Alignments at the instance-level cross-fertilize with alignments at the schema-level. Thereby, our system provides a truly holistic solution to the problem of ontology alignment. The heart of the approach is probabilistic. This allows PARIS to run without any parameter tuning. We demonstrate the efficiency of the algorithm and its precision through extensive experiments. In particular, we obtain a precision of around 90% in experiments with two of the world's largest ontologies.


Hybrid Trust Model for Internet Routing

Abstract: The current Internet is based on a fundamental assumption of reliability and good intent among actors in the network. Unfortunately, unreliable and malicious behaviour is becoming a major obstacle for Internet communication. In order to improve the trustworthiness and reliability of the network infrastructure, we propose a novel trust model to be incorporated into BGP routing. In our approach, trust model is defined by combining voting and recommendation to direct trust estimation for neighbour routers located in different autonomous systems. We illustrate the impact of our approach with cases that demonstrate the indication of distrusted paths beyond the nearest neighbours and the detection of a distrusted neighbour advertising a trusted path. We simulated the impact of weighting voted and direct trust in a rectangular grid of 15*15 nodes (autonomous systems) with a randomly connected topology.


Finite First Hitting Time versus Stochastic Convergence in Particle Swarm Optimisation

Abstract: We reconsider stochastic convergence analyses of particle swarm optimisation, and point out that previously obtained parameter conditions are not always sufficient to guarantee mean square convergence to a local optimum. We show that stagnation can in fact occur for non-trivial configurations in non-optimal parts of the search space, even for simple functions like SPHERE. The convergence properties of the basic PSO may in these situations be detrimental to the goal of optimisation, to discover a sufficiently good solution within reasonable time. To characterise optimisation ability of algorithms, we suggest the expected first hitting time (FHT), i.e., the time until a search point in the vicinity of the optimum is visited. It is shown that a basic PSO may have infinite expected FHT, while an algorithm introduced here, the Noisy PSO, has finite expected FHT on some functions.


A Frequency-domain Compensation Scheme for IQ-Imbalance in OFDM Receivers

Abstract: A pilot pattern across two OFDM symbols with special structure is devised for channel estimation in OFDM systems with IQ imbalance at receiver. Based on this pilot pattern, a high-efficiency time-domain (TD) least square (LS) channel estimator is proposed to significantly suppress channel noise by a factor N/(L+1) in comparison with the frequency-domain LS one in [1] where N and L+1 are the total number of subcarriers and the length of cyclic prefix, respectively. Following this, a low-complexity frequency-domain (FD) Gaussian elimination (GE) equalizer is proposed to eliminate IQ distortion by using only 2N complex multiplications per OFDM symbol. From simulation, the proposed scheme TD-LS/FD-GE using only two pilot OFDM symbols achieves the same bit error rate (BER) performance under ideal channel knowledge and no IQ imbalances at low and medium signal-to-noise ratio (SNR) regions whereas these compensation schemes including FD-LS/Post-FFT LS, FD-LS/Pre-FFT Corr, and SPP/Pre-FFT Corr in [1] require about twenty OFDM training symbols to reach the same performance where A/B denotes compensation scheme with A being channel estimator and B being equalizer.


Context Awarw Mobile Initiated Handoff for Performance Improvement in IEEE 802.11 Networks

Abstract: IEEE 802.11 is a widely used wireless LAN standard which offers a good bandwidth at low cost In an ESS, multiple APs can co-exist with overlapping coverage area. A mobile node connects to the AP from which it receives the best signal. Changes in traffic to and from different MNs occur over time. Load imbalance may develop on different APs. Throughput and delay of the different flows passing through the APs, where the load has increased beyond certain limit, may degrade. Different MNs associated to the overloaded APs will experience performance degradation. Overall performance of the ESS will also drop. In this paper we propose a scheme where MNs experiencing degraded performance will initiate action and with assistance from the associate AP perform handoff to less loaded AP within its range to improve performance.


Kernel Belief Propagation

Abstract: We propose a nonparametric generalization of belief propagation, Kernel Belief Propagation (KBP), for pairwise Markov random fields. Messages are represented as functions in a reproducing kernel Hilbert space (RKHS), and message updates are simple linear operations in the RKHS. KBP makes none of the assumptions commonly required in classical BP algorithms: the variables need not arise from a finite domain or a Gaussian distribution, nor must their relations take any particular parametric form. Rather, the relations between variables are represented implicitly, and are learned nonparametrically from training data. KBP has the advantage that it may be used on any domain where kernels are defined (Rd, strings, groups), even where explicit parametric models are not known, or closed form expressions for the BP updates do not exist. The computational cost of message updates in KBP is polynomial in the training data size. We also propose a constant time approximate message update procedure by representing messages using a small number of basis functions. In experiments, we apply KBP to image denoising, depth prediction from still images, and protein configuration prediction: KBP is faster than competing classical and nonparametric approaches (by orders of magnitude, in some cases), while providing significantly more accurate results.


Quality of Service Provisioning in Manet Using a Cross-Layer Approach for Routing

Abstract: Deployment of multimedia applications warrants provisioning of Quality of Service (QoS) in MANET. However, limited battery power, other resource constraints and mobility of nodes make QoS provisioning difficult to achieve in MANET. This difficulty can be overcome by using a cross-layer approach for routing. In [1] Patil et al., proposed a cross-layer routing protocol named Cost Based Power Aware Cross Layer - AODV (CPACL-AODV) which overcomes the limitation of battery power of nodes. Though many similar energy efficient and cross-layer routing protocols have been proposed for MANET, none of them handles QoS. A novel MANET routing protocol, Type of Service, Power and Bandwidth Aware AODV (TSPBA-AODV), which overcomes resource constraints and simultaneously provides QoS guarantees using a cross-layer approach, is proposed in this paper. In addition the effect of variation in nodes' mobility on performance of TSPBA-AODV is compared with that of CPACL-AODV [1] for two different types of network traffic. As shown by the results of simulations performed, TSPBA-AODV performs better than CPACL-AODV for MANET in which nodes move with small speeds (speeds up to 40 Km/hr approx.). In addition the effect of variation in data sending rate of nodes on performance of the protocols is also studied. As shown by the results of simulations performed, TSPBA-AODV performs better than CPACL-AODV for all variations in data sending rate of nodes.


A Survey of Routing Attacks and Security Measures in Mobile Ad-Hoc Networks

Abstract: Mobile ad hoc networks (MANETs) are a set of mobile nodes which are self-configuring and connected by wireless links automatically as per the defined routing protocol. The absence of a central management agency or a fixed infrastructure is a key feature of MANETs. These nodes communicate with each other by interchange of packets, which for those nodes not in wireless range goes hop by hop. Due to lack of a defined central authority, securitizing the routing process becomes a challenging task thereby leaving MANETs vulnerable to attacks, which results in deterioration in the performance characteristics as well as raises a serious question mark about the reliability of such networks. In this paper we have attempted to present an overview of the routing protocols, the known routing attacks and the proposed countermeasures to these attacks in various works.


High Quality of Service on Video Streaming in P2P Networks using FST-MDC

Abstract: Video streaming applications have newly attracted a large number of participants in a distribution network. Traditional client-server based video streaming solutions sustain precious bandwidth provision rate on the server. Recently, several P2P streaming systems have been organized to provide on-demand and live video streaming services on the wireless network at reduced server cost. Peer-to-Peer (P2P) computing is a new pattern to construct disseminated network applications. Typical error control techniques are not very well matched and on the other hand error prone channels has increased greatly for video transmission e.g., over wireless networks and IP. These two facts united together provided the essential motivation for the development of a new set of techniques (error concealment) capable of dealing with transmission errors in video systems. In this paper, we propose an flexible multiple description coding method named as Flexible Spatial-Temporal (FST) which improves error resilience in the sense of frame loss possibilities over independent paths. It introduces combination of both spatial and temporal concealment technique at the receiver and to conceal the lost frames more effectively. Experimental results show that, proposed approach attains reasonable quality of video performance over P2P wireless network.


Complexity of and Algorithms for Borda Manipulation

Abstract: We prove that it is NP-hard for a coalition of two manipulators to compute how to manipulate the Borda voting rule. This resolves one of the last open problems in the computational complexity of manipulating common voting rules. Because of this NP-hardness, we treat computing a manipulation as an approximation problem where we try to minimize the number of manipulators. Based on ideas from bin packing and multiprocessor scheduling, we propose two new approximation methods to compute manipulations of the Borda rule. Experiments show that these methods significantly outperform the previous best known %existing approximation method. We are able to find optimal manipulations in almost all the randomly generated elections tested. Our results suggest that, whilst computing a manipulation of the Borda rule by a coalition is NP-hard, computational complexity may provide only a weak barrier against manipulation in practice.


Scale-Invariant Local Descriptor for Event Recognition in 1D Sensor Signals

Abstract: In this paper, we introduce a shape-based, time-scale invariant feature descriptor for 1-D sensor signals. The time-scale invariance of the feature allows us to use feature from one training event to describe events of the same semantic class which may take place over varying time scales such as walking slow and walking fast. Therefore it requires less training set. The descriptor takes advantage of the invariant location detection in the scale space theory and employs a high level shape encoding scheme to capture invariant local features of events. Based on this descriptor, a scale-invariant classifier with "R" metric (SIC-R) is designed to recognize multi-scale events of human activities. The R metric combines the number of matches of keypoint in scale space with the Dynamic Time Warping score. SICR is tested on various types of 1-D sensors data from passive infrared, accelerometer and seismic sensors with more than 90% classification accuracy.


Improving Anonymity in Shared Key Primitives Based on Perfect Hash Families

Abstract: We propose a new scheme for sharing symmetric key operations among a set of participants according to a (t,n) threshold access structure. We focus on anonymity properties of this scheme and show that this scheme provides improved values of anonymity measures than the existing ones. In particular, the scheme can provide optimal and equitable participant anonymity when it is based on balanced perfect hash families.


Towards High-Performance Network Application Identification With Aggregate-Flow Cache

Abstract: Classifying network traffic according to their application-layer protocols is an important task in modern networks for traffic management and network security. Existing payload-based or statistical methods of application identification cannot meet the demand of both high performance and accurate identification at the same time. We propose an application identification framework that classifies traffic at aggregate-flow level leveraging aggregate-flow cache. A detailed traffic classifier designed based on this framework is illustrated to improve the throughput of payload-based identification methods. We further optimize the classifier by proposing an efficient design of aggregate-flow cache. The cache design employs a frequency-based, recency-aware replacement algorithm based on the analysis of temporal locality of aggregate-flow cache. Experiments on real-world traces show that our traffic classifier with aggregate-flow cache can reduce up to 95% workload of backend identification engine. The proposed cache replacement algorithm outperforms well-known replacement algorithms, and achieves 90% of the optimal performance using only 15% of memory. The throughput of a payload-based identification system, L7-filter [1], is increased by up to 5.1 times by using our traffic classifier design.


Relational Schema Protocol (RSP)

Abstract: This document specifies the Relational Schema Protocol (RSP). RSP enables loosely coupled applications to share and exchange relational data. It defines fixed message format for an arbitrary relational schema so that the changes in the data schema do not affect the message format. This prevents the interacting applications from having to be reimplemented during the data schema evolvement.


Clustering and Classification in Text Collections Using Graph Modularity

Abstract: A new fast algorithm for clustering and classification of large collections of text documents is introduced. The new algorithm employs the bipartite graph that realizes the word-document matrix of the collection. Namely, the modularity of the bipartite graph is used as the optimization functional. Experiments performed with the new algorithm on a number of text collections had shown a competitive quality of the clustering (classification), and a record-breaking speed.


A Combination of Testability and Decodability by Tensor Products

Abstract: Ben-Sasson and Sudan (RSA 2006) showed that repeated tensor products of linear codes with a very large distance are locally testable. Due to the requirement of a very large distance the associated tensor products could be applied only over sufficiently large fields. Then Meir (SICOMP 2009) used this result (as a black box) to present a combinatorial construction of locally testable codes that match best known parameters. As a consequence, this construction was obtained over sufficiently large fields. In this paper we improve the result of Ben-Sasson and Sudan and show that for \emph{any} linear codes the associated tensor products are locally testable. Consequently, the construction of Meir can be taken over any field, including the binary field. Moreover, a combination of our result with the result of Spielman (IEEE IT, 1996) implies a construction of linear codes (over any field) that combine the following properties: have constant rate and constant relative distance; have blocklength $n$ and testable with $n^{\epsilon}$ queries, for any constant $\epsilon > 0$; linear time encodable and linear-time decodable from a constant fraction of errors. Furthermore, a combination of our result with the result of Guruswami et al. (STOC 2009) implies a similar corollary regarding the list-decodable codes.


Robot Networks with Homonyms: The Case of Patterns Formation

Abstract: In this paper, we consider the problem of formation of a series of geometric patterns [4] by a network of oblivious mobile robots that communicate only through vision. So far, the problem has been studied in models where robots are either assumed to have distinct identifiers or to be completely anonymous. To generalize these results and to better understand how anonymity affects the computational power of robots, we study the problem in a new model, introduced recently in [5], in which n robots may share up to 1 <= h <= n different identifiers. We present necessary and sufficient conditions, relating symmetricity and homonymy, that makes the problem solvable. We also show that in the case where h = n, making the identifiers of robots invisible does not limit their computational power. This contradicts a result of [4]. To present our algorithms, we use a function that computes the Weber point for many regular and symmetric configurations. This function is interesting in its own right, since the problem of finding Weber points has been solved up to now for only few other patterns.


Revealing digital documents. Concealed structures in data

Abstract: This short paper gives an introduction to a research project to analyze how digital documents are structured and described. Using a phenomenological approach, this research will reveal common patterns that are used in data, independent from the particular technology in which the data is available. The ability to identify these patterns, on different levels of description, is important for several applications in digital libraries. A better understanding of data structuring will not only help to better capture singular characteristics of data by metadata, but will also recover intended structures of digital objects, beyond long term preservation.


Publicity of the intimate text (the blog studying and publication)

Abstract: One of the important problems of a modern society - communications. At all readiness of this question both humanitarian, and engineering science, process of transfer and information reception remains in the centre of attention of researchers. The dialogue phenomenon in a network becomes the significant factor of such attention. The fact of the publication of blogs and increasing popularity of bloggers is connected, in our opinion, with an increasing openness of a blog sphere (each record can be commented any user), and accordingly, the Internet as a whole.