Philosophy of the M<Emphasis Type="SmallCaps">ini</Emphasis>Z<Emphasis Type="SmallCaps">inc</Emphasis> challenge

MiniZinc arose as a response to the extended discussion at CP2006 of the need for a standard modelling language for CP. This is a challenging problem, and we believe MiniZinc makes a good attempt to handle the most obvious obstacle: there are hundreds of potential global constraints, most handled by few or no systems. A standard input language for solvers gives us the capability to compare different solvers. Hence, every year since 2008 we have run the MiniZinc Challenge comparing different solvers that support MiniZinc. In this report we discuss the philosophy behind the challenge, why we do it, how we do it, and why we do it that way.     

Aggregate features and A<Emphasis Type="SmallCaps">DA</Emphasis>B<Emphasis Type="SmallCaps">OOST</Emphasis> for music classification

We present an algorithm that predicts musical genre and artist from an audio waveform. Our method uses the ensemble learner ADABOOST to select from a set of audio features that have been extracted from segmented audio and then aggregated. Our classifier proved to be the most effective method for genre classification at the recent MIREX 2005 international contests in music information extraction, and the second-best method for recognizing artists. This paper describes our method in detail, from feature extraction to song classification, and presents an evaluation of our method on three genre databases and two artist-recognition databases. Furthermore, we present evidence collected from a variety of popular features and classifiers that the technique of classifying features aggregated over segments of audio is better than classifying either entire songs or individual short-timescale features. Editor: Gerhard Widmer     

Leveraging annotation-based modeling with <Emphasis Type="SmallCaps">Jump</Emphasis>

The capability of UML profiles to serve as annotation mechanism has been recognized in both research and industry. Today’s modeling tools offer profiles specific to platforms, such as Java, as they facilitate model-based engineering approaches. However, considering the large number of possible annotations in Java, manually developing the corresponding profiles would only be achievable by huge development and maintenance efforts. Thus, leveraging annotation-based modeling requires an automated approach capable of generating platform-specific profiles from Java libraries. To address this challenge, we present the fully automated transformation chain realized by Jump, thereby continuing existing mapping efforts between Java and UML by emphasizing on annotations and profiles. The evaluation of Jump shows that it scales for large Java libraries and generates profiles of equal or even improved quality compared to profiles currently used in practice. Furthermore, we demonstrate the practical value of Jump by contributing profiles that facilitate reverse engineering and forward engineering processes for the Java platform by applying it to a modernization scenario.     

<Emphasis Type="SmallCaps">OpenCL</Emphasis> Based Parallel Algorithm for RBF-PUM Interpolation

We present a parallel algorithm for multivariate Radial Basis Function Partition of Unity Method (RBF-PUM) interpolation. The concurrent nature of the RBF-PUM enables designing parallel algorithms for dealing with a large number of scattered data-points in high space dimensions. To efficiently exploit this concurrency, our algorithm makes use of shared-memory parallel processors through the OpenCL standard. This efficiency is achieved by a parallel space partitioning strategy with linear computational time complexity with respect to the input and evaluation points. The speed of our approach allows for computationally more intensive construction of the interpolant. In fact, the RBF-PUM can be coupled with a cross-validation technique that searches for optimal values of the shape parameters associated with each local RBF interpolant, thus reducing the global interpolation error. The numerical experiments support our claims by illustrating the interpolation errors and the running times of our algorithm.     

Three generalizations of the <Emphasis Type="SmallCaps">FOCUS</Emphasis> constraint

The FOCUS constraint expresses the notion that solutions are concentrated. In practice, this constraint suffers from the rigidity of its semantics. To tackle this issue, we propose three generalizations of the FOCUS constraint. We provide for each one a complete filtering algorithm. Moreover, we propose ILP and CSP decompositions.     

Maximum <Emphasis Type="Italic">k</Emphasis>-Splittable <Emphasis Type="Italic">s</Emphasis>,<Emphasis Type="Italic">t</Emphasis>-Flows

Given a graph with a source and a sink node, the NP-hard maximum k-splittable s,t-flow (M k SF) problem is to find a flow of maximum value from s to t with a flow decomposition using at most k paths. The multicommodity variant of this problem is a natural generalization of disjoint paths and unsplittable flow problems. Constructing a k-splittable flow requires two interdepending decisions. One has to decide on k paths (routing) and on the flow values for the paths (packing). We give efficient algorithms for computing exact and approximate solutions by decoupling the two decisions into a first packing step and a second routing step. Usually the routing is considered before the packing. Our main contributions are as follows: (i) We show that for constant k a polynomial number of packing alternatives containing at least one packing used by an optimal M k SF solution can be constructed in polynomial time. If k is part of the input, we obtain a slightly weaker result. In this case we can guarantee that, for any fixed ε>0, the computed set of alternatives contains a packing used by a (1−ε)-approximate solution. The latter result is based on the observation that (1−ε)-approximate flows only require constantly many different flow values. We believe that this observation is of interest in its own right. (ii) Based on (i), we prove that, for constant k, the M k SF problem can be solved in polynomial time on graphs of bounded treewidth. If k is part of the input, this problem is still NP-hard and we present a polynomial time approximation scheme for it.     

<Emphasis Type="SmallCaps">Trasgo</Emphasis>: a nested-parallel programming system

Programming models of pure nested-parallelism are appealing due to their ease of programming and good analysis and debugging properties. Although their simple synchronization structure is appropriate to represent abstract parallel algorithms, it does not take into account many implementation issues. In this work we present Trasgo, a programming system based on high-level, nested-parallel specifications. We show how it allows to easily express complex combinations of data and task parallelism with a common scheme, hiding the layout and scheduling details. The approach allows the development of a modular compiler where automatic transformation techniques may exploit lower level and more complex synchronization structures, unlocking the limitations of pure nested-parallel programming. This article presents an overview of the features of Trasgo, and its architecture. We present some performance results using well-known parallel algorithms, and a roadmap of improvements and new features to be added to Trasgo.     

Extension of <Emphasis Type="Italic">O</Emphasis>(<Emphasis Type="Italic">n</Emphasis> log <Emphasis Type="Italic">n</Emphasis>) Filtering Algorithms for the Unary Resource Constraint to Optional Activities

Scheduling is one of the most successful application areas of constraint programming mainly thanks to special global constraints designed to model resource restrictions. Among these global constraints, edge-finding and not-first/not-last are the most popular filtering algorithms for unary resources. In this paper we introduce new O(n log n) versions of these two filtering algorithms and one more O(n log n) filtering algorithm called detectable precedences. These algorithms use a special data structures Θ-tree and Θ-Λ-tree. These data structures are especially designed for “what-if” reasoning about a set of activities so we also propose to use them for handling so called optional activities, i.e. activities which may or may not appear on the resource. In particular, we propose new O(n log n) variants of filtering algorithms which are able to handle optional activities: overload checking, detectable precedences and not-first/not-last.     

Approximation Algorithms for <Emphasis Type="Italic">k</Emphasis>-hurdle Problems

The polynomial-time solvable k-hurdle problem is a natural generalization of the classical s-t minimum cut problem where we must select a minimum-cost subset S of the edges of a graph such that |p∩S|≥k for every s-t path p. In this paper, we describe a set of approximation algorithms for “k-hurdle” variants of the NP-hard multiway cut and multicut problems. For the k-hurdle multiway cut problem with r terminals, we give two results, the first being a pseudo-approximation algorithm that outputs a (k−1)-hurdle solution whose cost is at most that of an optimal solution for k hurdles. Secondly, we provide a 2(1-\frac1r)2(1-\frac{1}{r})-approximation algorithm based on rounding the solution of a linear program, for which we give a simple randomized half-integrality proof that works for both edge and vertex k-hurdle multiway cuts that generalizes the half-integrality results of Garg et al. for the vertex multiway cut problem. We also describe an approximation-preserving reduction from vertex cover as evidence that it may be difficult to achieve a better approximation ratio than 2(1-\frac1r)2(1-\frac{1}{r}). For the k-hurdle multicut problem in an n-vertex graph, we provide an algorithm that, for any constant ε>0, outputs a ⌈(1−ε)k⌉-hurdle solution of cost at most O(log n) times that of an optimal k-hurdle solution, and we obtain a 2-approximation algorithm for trees.     

Heidegger on technology and <Emphasis Type="Italic">Gelassenheit</Emphasis>: <Emphasis Type="Italic">wabi</Emphasis>-<Emphasis Type="Italic">sabi</Emphasis> and the art of <Emphasis Type="Italic">Verfallenheit</Emphasis>

The question of the contemporary relevance of Heidegger’s reflections on technology to today’s advanced technology is here explored with reference to the notion of “entanglement” towards a review of Heidegger’s understanding of technology and media, including the entertainment industry and modern digital life. Heidegger’s reflections on Gelassenheit have been connected with the aesthetics of the tea ceremony, disputing the material aesthetics of porcelain versus plastic. Here by approaching the art of wabi-sabi as the art of Verfallenheit, I argue that Gelassenheit may be understood in these terms.     

<Emphasis Type="Italic">t</Emphasis>-Private and <Emphasis Type="Italic">t</Emphasis>-Secure Auctions

In most of the auction systems the values of bids are known to the auctioneer. This allows him to manipulate the outcome of the auction. Hence, one might be interested in hiding these values. Some cryptographically secure protocols for electronic auctions have been presented in the last decade. Our work extends these protocols in several ways. On the basis of garbled circuits, i.e., encrypted circuits, we present protocols for sealed-bid auctions that fulfill the following requirements： 1） protocols are information-theoretically t-private for honest but curious parties; 2） the number of bits that can be learned by malicious adversaries is bounded by the output length of the auction; 3） the computational requirements for participating parties are very low： only random bit choices and bitwise computation of the XOR-function are necessary. Note that one can distinguish between the protocol that generates a garbled circuit for an auction and the protocol to evaluate the auction. In this paper we address both problems. We will present a t-private protocol for the construction of a garbled circuit that reaches the lower bound of 2t ＋ 1 parties, and Finally, we address the problem of bid changes in an auction. a more randomness efficient protocol for （t ＋ 1）^2 parties     

Corepresentation of a sylow <Emphasis Type="Italic">p</Emphasis>-subgroup of a group <Emphasis Type="Italic">S</Emphasis><Subscript><Emphasis Type="Italic">n</Emphasis></Subscript>

The corepresentation of a Sylow p-subgroup of a symmetric group in the form of generating relations is investigated, and a Sylow subgroup of a group , i.e., an n-fold wreath product of regular cyclic groups of prime order, that is isomorphic to the group of automorphisms of a spherically homogeneous root tree is also studied. Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 27–41, January–February 2009.     

<Emphasis Type="Italic">I-Quest</Emphasis>: an <Emphasis Type="Italic">i</Emphasis>ntelligent <Emphasis Type="Italic">que</Emphasis>ry <Emphasis Type="Italic">st</Emphasis>ructuring based on user browsing feedback for semantic retrieval of video data

In spite of significant improvements in video data retrieval, a system has not yet been developed that can adequately respond to a user’s query. Typically, the user has to refine the query many times and view query results until eventually the expected videos are retrieved from the database. The complexity of video data and questionable query structuring by the user aggravates the retrieval process. Most previous research in this area has focused on retrieval based on low-level features. Managing imprecise queries using semantic (high-level) content is no easier than queries based on low-level features due to the absence of a proper continuous distance function. We provide a method to help users search for clips and videos of interest in video databases. The video clips are classified as interesting and uninteresting based on user browsing. The attribute values of clips are classified by commonality, presence, and frequency within each of the two groups to be used in computing the relevance of each clip to the user’s query. In this paper, we provide an intelligent query structuring system, called I-Quest, to rank clips based on user browsing feedback, where a template generation from the set of interesting and uninteresting sets is impossible or yields poor results.

EP<Emphasis Type="Italic">n</Emphasis>P: An Accurate <Emphasis Type="Italic">O</Emphasis>(<Emphasis Type="Italic">n</Emphasis>) Solution to the P<Emphasis Type="Italic">n</Emphasis>P Problem

We propose a non-iterative solution to the PnP problem—the estimation of the pose of a calibrated camera from n 3D-to-2D point correspondences—whose computational complexity grows linearly with n. This is in contrast to state-of-the-art methods that are O(n ⁵) or even O(n ⁸), without being more accurate. Our method is applicable for all n≥4 and handles properly both planar and non-planar configurations. Our central idea is to express the n 3D points as a weighted sum of four virtual control points. The problem then reduces to estimating the coordinates of these control points in the camera referential, which can be done in O(n) time by expressing these coordinates as weighted sum of the eigenvectors of a 12×12 matrix and solving a small constant number of quadratic equations to pick the right weights. Furthermore, if maximal precision is required, the output of the closed-form solution can be used to initialize a Gauss-Newton scheme, which improves accuracy with negligible amount of additional time. The advantages of our method are demonstrated by thorough testing on both synthetic and real-data.     

<Emphasis Type="SmallCaps">Resling</Emphasis>: a scalable and generic framework to mine top-<Emphasis Type="Italic">k</Emphasis> representative subgraph patterns

Many datasets include missing values in their attributes. Data mining techniques are not applicable in the presence of missing values. So an important step in preprocessing of a data mining task is missing value management. One of the most important categories in missing value management techniques is missing value imputation. This paper presents a new imputation technique. The proposed imputation technique is based on statistical measurements. The suggested imputation technique employs an ensemble of the estimators built to estimate the missing values based on positive and negative correlated observed attributes separately. Each estimator guesses a value for a missed value based on the average and variance of that feature. The average and variance of the feature are estimated from the non-missed values of that feature. The final consensus value for a missed value is the weighted aggregation of the values estimated by different estimators. The chief weight is attribute correlation, and the slight weight is dependent to kernel function such as kurtosis, skewness, number of involved samples and composition of them. The missing values are deliberately produced randomly at different levels. The experimentations indicate that the suggested technique has a good accuracy in comparison with the classical methods.     

Low-density incomplete <Emphasis Type="Italic">LDL</Emphasis><Superscript><Emphasis Type="Italic">T</Emphasis></Superscript> factorizations

A particular class of incomplete factorizations is proposed as preconditioners for the linear system Ax = b where A is a symmetric, large and sparse matrix. The ILDL ^T< (p) factorization (p = 1,2,3, …) determines the density of the lower triangular matrix L selecting the p largest off-diagonal entries of each column during the Gaussian elimination process. This selection may be computationally expensive, but the effectiveness of the preconditioner allows us to choose very low-density factors to reduce both work time and storage requirements. This incomplete factorization can be performed reliably on H-matrices. When A is a positive definite matrix, but not an H-matrix, one can perform an incomplete factorization if positive off-diagonal entries are removed or reduced and diagonally compensated. Numerical results for a variety of problems and comparisons with other incomplete factorizations are presented. Received: August 2002 / Accepted: December 2002 RID="*" ID="*"This work was supported by the Spanish grant BFM 2001-2641.     

Intuitionistic (<Emphasis Type="Italic">S</Emphasis>, <Emphasis Type="Italic">T</Emphasis>)-fuzzy hyperquasigroups

The concept of intuitionistic fuzzy subhyperquasigroups in a hyperquasigroup with respect to an s-norm and a t-norm on intuitionistic fuzzy sets is introduced and their properties of such hyperquasigroups are studied. Intuitionistic (S, T)-fuzzy relations on a hyperquasigroup G are discussed. In particular, we investigate connections hyperquasigroups with binary quasigroups.     

Software Defect Detection with R<Emphasis Type="SmallCaps">ocus</Emphasis>

Software defect detection aims to automatically identify defective software modules for efficient software test in order to improve the quality of a software system.Although many machine learning methods have been successfully applied to the task,most of them fail to consider two practical yet important issues in software defect detection.First,it is rather difficult to collect a large amount of labeled training data for learning a well-performing model;second,in a software system there are usually much fewer defective modules than defect-free modules,so learning would have to be conducted over an imbalanced data set.In this paper,we address these two practical issues simultaneously by proposing a novel semi-supervised learning approach named Rocus.This method exploits the abundant unlabeled examples to improve the detection accuracy,as well as employs under-sampling to tackle the class-imbalance problem in the learning process.Experimental results of real-world software defect detection tasks show that Rocus is effective for software defect detection.Its performance is better than a semi-supervised learning method that ignores the class-imbalance nature of the task and a class-imbalance learning method that does not make effective use of unlabeled data.     

Participant selection for <Emphasis Type="Italic">t</Emphasis>-sweep <Emphasis Type="Italic">k</Emphasis>-coverage crowd sensing tasks

With the popularization of wireless networks and mobile intelligent terminals, mobile crowd sensing is becoming a promising sensing paradigm. Tasks are assigned to users with mobile devices, which then collect and submit ambient information to the server. The composition of participants greatly determines the quality and cost of the collected information. This paper aims to select fewest participants to achieve the quality required by a sensing task. The requirement namely “t-sweep k-coverage” means for a target location, every t time interval should at least k participants sense. The participant selection problem for “t-sweep k-coverage” crowd sensing tasks is NP-hard. Through delicate matrix stacking, linear programming can be adopted to solve the problem when it is in small size. We further propose a participant selection method based on greedy strategy. The two methods are evaluated through simulated experiments using users’ call detail records. The results show that for small problems, both the two methods can find a participant set meeting the requirement. The number of participants picked by the greedy based method is roughly twice of the linear programming based method. However, when problems become larger, the linear programming based method performs unstably, while the greedy based method can still output a reasonable solution.