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.     

Approximate distributed top-<Emphasis Type="Italic">k</Emphasis> queries

We consider a distributed system where each node keeps a local count for items (similar to elections where nodes are ballot boxes and items are candidates). A top-k query in such a system asks which are the k items whose global count, across all nodes in the system, is the largest. In this paper, we present a Monte Carlo algorithm that outputs, with high probability, a set of k candidates which approximates the top-k items. The algorithm is motivated by sensor networks in that it focuses on reducing the individual communication complexity. In contrast to previous algorithms, the communication complexity depends only on the global scores and not on the partition of scores among nodes. If the number of nodes is large, our algorithm dramatically reduces the communication complexity when compared with deterministic algorithms. We show that the complexity of our algorithm is close to a lower bound on the cell-probe complexity of any non-interactive top-k approximation algorithm. We show that for some natural global distributions (such as the Geometric or Zipf distributions), our algorithm needs only polylogarithmic number of communication bits per node. An extended abstract of this paper appeared in Proc. 13th Int. Colloquium on Structural Information and Communication Complexity, SIROCCO 2006, Lecture Notes in Computer Science 4056, pp. 319–333.     

On the <Emphasis Type="Italic">k</Emphasis>-accessibility of cores of <Emphasis Type="Italic">TU</Emphasis>-cooperative games

This paper proposes a strengthening of the author’s core-accessibility theorem for balanced TU-cooperative games. The obtained strengthening relaxes the influence of the nontransitivity of classical domination αv on the quality of the sequential improvement of dominated imputations in a game v. More specifically, we establish the k-accessibility of the core C(α _v ) of any balanced TU-cooperative game v for all natural numbers k: for each dominated imputation x, there exists a converging sequence of imputations x₀, x₁,..., such that x₀ = x, lim x _r ∈ C(α _v ) and x_r?m is dominated by any successive imputation x _r with m ∈ [1, k] and r ≥ m. For showing that the TU-property is essential to provide the k-accessibility of the core, we give an example of an NTU-cooperative game G with a ”black hole” representing a nonempty closed subset B ? G(N) of dominated imputations that contains all the α _G -monotonic sequential improvement trajectories originating at any point x ∈ B.     

Analysis of Approximation Algorithms for <Emphasis Type="Italic">k</Emphasis>-Set Cover Using Factor-Revealing Linear Programs

We present new combinatorial approximation algorithms for the k-set cover problem. Previous approaches are based on extending the greedy algorithm by efficiently handling small sets. The new algorithms further extend these approaches by utilizing the natural idea of computing large packings of elements into sets of large size. Our results improve the previously best approximation bounds for the k-set cover problem for all values of k≥6. The analysis technique used could be of independent interest; the upper bound on the approximation factor is obtained by bounding the objective value of a factor-revealing linear program. A preliminary version of the results in this paper appeared in Proceedings of the 16th International Symposium on Fundamentals of Computation Theory (FCT ’07), LNCS 4639, Springer, pp. 52–63, 2007. This work was partially supported by the European Union under IST FET Integrated Project 015964 AEOLUS and by the General Secretariat for Research and Technology of the Greek Ministry of Development under programme PENED 2003.     

Supporting top-<Emphasis Type="Italic">k</Emphasis><Emphasis Type="Italic">join</Emphasis> queries in relational databases

Ranking queries, also known as top-k queries, produce results that are ordered on some computed score. Typically, these queries involve joins, where users are usually interested only in the top-k join results. Top-k queries are dominant in many emerging applications, e.g., multimedia retrieval by content, Web databases, data mining, middlewares, and most information retrieval applications. Current relational query processors do not handle ranking queries efficiently, especially when joins are involved. In this paper, we address supporting top-k join queries in relational query processors. We introduce a new rank-join algorithm that makes use of the individual orders of its inputs to produce join results ordered on a user-specified scoring function. The idea is to rank the join results progressively during the join operation. We introduce two physical query operators based on variants of ripple join that implement the rank-join algorithm. The operators are nonblocking and can be integrated into pipelined execution plans. We also propose an efficient heuristic designed to optimize a top-k join query by choosing the best join order. We address several practical issues and optimization heuristics to integrate the new join operators in practical query processors. We implement the new operators inside a prototype database engine based on PREDATOR. The experimental evaluation of our approach compares recent algorithms for joining ranked inputs and shows superior performance.Received: 23 December 2003, Accepted: 31 March 2004, Published online: 12 August 2004Edited by: S. AbiteboulExtended version of the paper published in the Proceedings of the 29th International Conference on Very Large Databases, VLDB 2003, Berlin, Germany, pp 754-765     

Multivariate fuzzy <Emphasis Type="Italic">k</Emphasis>-modes algorithm

In the fuzzy k-modes clustering, there is just one membership degree of interest by class for each individual which cannot be sufficient to model ambiguity of data precisely. It is known that the essence of a multivariate thinking allows to expose the inherent structure and meaning revealed within a set of variables classified. In this paper, a multivariate approach for membership degrees is presented to better handle ambiguous data that share properties of different clusters. This method is compared with other fuzzy k-modes methods of the literature based on a multivariate internal index that is also proposed in this paper. Synthetic and real categorical data sets are considered in this study.     

Multiple <Emphasis Type="Italic">k</Emphasis> nearest neighbor search

The problem of kNN (k Nearest Neighbor) queries has received considerable attention in the database and information retrieval communities. Given a dataset D and a kNN query q, the k nearest neighbor algorithm finds the closest k data points to q. The applications of kNN queries are board, not only in spatio-temporal databases but also in many areas. For example, they can be used in multimedia databases, data mining, scientific databases and video retrieval. The past studies of kNN query processing did not consider the case that the server may receive multiple kNN queries at one time. Their algorithms process queries independently. Thus, the server will be busy with continuously reaccessing the database to obtain the data that have already been acquired. This results in wasting I/O costs and degrading the performance of the whole system. In this paper, we focus on this problem and propose an algorithm named COrrelated kNN query Evaluation (COKE). The main idea of COKE is an “information sharing” strategy whereby the server reuses the query results of previously executed queries for efficiently processing subsequent queries. We conduct a comprehensive set of experiments to analyze the performance of COKE and compare it with the Best-First Search (BFS) algorithm. Empirical studies indicate that COKE outperforms BFS, and achieves lower I/O costs and less running time.     

Pseudo-Randomness of Certain Sequences of <Emphasis Type="Italic">k</Emphasis> Symbols with Length <Emphasis Type="Italic">pq</Emphasis>

The theory of finite pseudo-random binary sequences was built by C. Mauduit and A. Sárközy and later extended to sequences of k symbols (or k-ary sequences). Certain constructions of pseudo-random sequences of k symbols were presented over finite fields in the literature. In this paper, two families of sequences of k symbols are constructed by using the integers modulo pq for distinct odd primes p and q. The upper bounds on the well-distribution measure and the correlation measure of the families sequences are presented in terms of certain character sums over modulo pq residue class rings. And low bounds on the linear complexity profile are also estimated.     

<Emphasis Type="Italic">k</Emphasis>-most suitable locations selection

Choosing the best location for starting a business or expanding an existing enterprize is an important issue. A number of location selection problems have been discussed in the literature. They often apply the Reverse Nearest Neighbor as the criterion for finding suitable locations. In this paper, we apply the Average Distance as the criterion and propose the so-called k-most suitable locations (k-MSL) selection problem. Given a positive integer k and three datasets: a set of customers, a set of existing facilities, and a set of potential locations. The k-MSL selection problem outputs k locations from the potential location set, such that the average distance between a customer and his nearest facility is minimized. In this paper, we formally define the k-MSL selection problem and show that it is NP-hard. We first propose a greedy algorithm which can quickly find an approximate result for users. Two exact algorithms are then proposed to find the optimal result. Several pruning rules are applied to increase computational efficiency. We evaluate the algorithms’ performance using both synthetic and real datasets. The results show that our algorithms are able to deal with the k-MSL selection problem efficiently.     

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.     

Adaptive (<Emphasis Type="Italic">k</Emphasis>, <Emphasis Type="Italic">F</Emphasis><Subscript>1</Subscript>) interpolation-based hiding scheme

In 2012, Lee et al. proposed an interpolation technique with neighboring pixels (INP) as the base to conceal secret information in predicted pixels. Their method can effectively predict the pixel between two neighboring pixels. However, the different lengths of secret messages caused great distortion when a large secret message was concealed in the predicted value. Therefore, the proposed scheme applies the center folding strategy to fold the secret message for reducing image distortion. Furthermore, the proposed scheme references the variance of the neighboring pixel to determine the length of the secret message for controlling image quality. The parameter pair (k, F ₁) is used to categorize the variance and determine the size of the secret message hidden in each category. k is the total number of thresholds which computed based on the characteristics of each image for balancing hiding payload and image quality. F ₁ is the length of the secret message for the smoothest area. The experimental results show that the embedding capacity of the proposed method is 1.5 bpp higher than that of existing methods. For the same hiding payload, the image quality of the proposed method is 1.6 dB higher than that of existing methods.     

Exact 3-satisfiability is decidable in time <Emphasis Type="Italic">O</Emphasis>(2<Superscript>0.16254<Emphasis Type="Italic">n</Emphasis></Superscript>)

Let F=C ₁C _m be a Boolean formula in conjunctive normal form over a set V of n propositional variables, s.t. each clause C _i contains at most three literals l over V. Solving the problem exact 3-satisfiability (X3SAT) for F means to decide whether there is a truth assignment setting exactly one literal in each clause of F to true (1). As is well known X3SAT is NP-complete [6]. By exploiting a perfect matching reduction we prove that X3SAT is deterministically decidable in time O(2^0.18674n ). Thereby we improve a result in [2,3] stating X3SATO(2^0.2072n ) and a bound of O(2^0.200002n ) for the corresponding enumeration problem #X3SAT stated in a preprint [1]. After that by a more involved deterministic case analysis we are able to show that X3SATO(2^0.16254n ).     

Exact bootstrap <Emphasis Type="Italic">k</Emphasis>-nearest neighbor learners

Bootstrap aggregation, or bagging, is a method of reducing the prediction error of a statistical learner. The goal of bagging is to construct a new learner which is the expectation of the original learner with respect to the empirical distribution function. In nearly all cases, the expectation cannot be computed analytically, and bootstrap sampling is used to produce an approximation. The k-nearest neighbor learners are exceptions to this generalization, and exact bagging of many k-nearest neighbor learners is straightforward. This article presents computationally simple and fast formulae for exact bagging of k-nearest neighbor learners and extends exact bagging methods from the conventional bootstrap sampling (sampling n observations with replacement from a set of n observations) to bootstrap sub-sampling schemes (with and without replacement). In addition, a partially exact k-nearest neighbor regression learner is developed. The article also compares the prediction error associated with elementary and exact bagging k-nearest neighbor learners, and several other ensemble methods using a suite of publicly available data sets.     

Providing <Emphasis Type="Italic">k</Emphasis>-anonymity in data mining

In this paper we present extended definitions of k-anonymity and use them to prove that a given data mining model does not violate the k-anonymity of the individuals represented in the learning examples. Our extension provides a tool that measures the amount of anonymity retained during data mining. We show that our model can be applied to various data mining problems, such as classification, association rule mining and clustering. We describe two data mining algorithms which exploit our extension to guarantee they will generate only k-anonymous output, and provide experimental results for one of them. Finally, we show that our method contributes new and efficient ways to anonymize data and preserve patterns during anonymization.     

The direction-constrained <Emphasis Type="Italic">k</Emphasis> nearest neighbor query

Finding k nearest neighbor objects in spatial databases is a fundamental problem in many geospatial systems and the direction is one of the key features of a spatial object. Moreover, the recent tremendous growth of sensor technologies in mobile devices produces an enormous amount of spatio-directional (i.e., spatially and directionally encoded) objects such as photos. Therefore, an efficient and proper utilization of the direction feature is a new challenge. Inspired by this issue and the traditional k nearest neighbor search problem, we devise a new type of query, called the direction-constrained k nearest neighbor (DCkNN) query. The DCkNN query finds k nearest neighbors from the location of the query such that the direction of each neighbor is in a certain range from the direction of the query. We develop a new index structure called MULTI, to efficiently answer the DCkNN query with two novel index access algorithms based on the cost analysis. Furthermore, our problem and solution can be generalized to deal with spatio-circulant dimensional (such as a direction and circulant periods of time such as an hour, a day, and a week) objects. Experimental results show that our proposed index structure and access algorithms outperform two adapted algorithms from existing kNN algorithms.     

An <Emphasis Type="Italic">R</Emphasis> || <Emphasis Type="Italic">C</Emphasis><Subscript>max</Subscript> Quantum Scheduling Algorithm

Grover’s search algorithm can be applied to a wide range of problems; even problems not generally regarded as searching problems, can be reformulated to take advantage of quantum parallelism and entanglement, and lead to algorithms which show a square root speedup over their classical counterparts. In this paper, we discuss a systematic way to formulate such problems and give as an example a quantum scheduling algorithm for an R||C_max problem. R||C_max is representative for a class of scheduling problems whose goal is to find a schedule with the shortest completion time in an unrelated parallel machine environment. Given a deadline, or a range of deadlines, the algorithm presented in this paper allows us to determine if a solution to an R||C_max problem with N jobs and M machines exists, and if so, it provides the schedule. The time complexity of the quantum scheduling algorithm is while the complexity of its classical counterpart is .     

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.     

Evolving antennas <Emphasis Type="Italic">in-situ</Emphasis>

Evolvable hardware, particularly evolvable electronic circuitry, is being vigorously explored as a means to enhance performance and survivability. However, antennas are also excellent candidates for evolvable hardware, as they are crucial components in any wireless system, and changes in the environment can seriously affect antenna performance. The evolvable antenna system in this paper shows that an evolutionary algorithm and a reconfigurable antenna can be used to optimize signal reception in-situ, i.e., in real surroundings, rather than in simulation or even on an antenna range. Several reconfigurable antenna topologies were tested, and all were able to outperform a conventional antenna.The research described in this paper was performed for the Evolvable Hardware program at the Center for Integrated Space Microsystems, Jet Propulsion Laboratory, California Institute of Technology, and was sponsored by the National Aeronautics and Space Administration. We are very grateful for their support. Thanks also to G.R. Freeth for his feedback regarding using AM signals to determine signal strength.     

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.     

An Efficient Clustering Algorithm for <Emphasis Type="Italic">k</Emphasis>-Anonymisation

K-anonymisation is an approach to protecting individuals from being identified from data.Good k-anonymisations should retain data utility and preserve privacy,but few methods have considered these two conflicting requirements together. In this paper,we extend our previous work on a clustering-based method for balancing data utility and privacy protection, and propose a set of heuristics to improve its effectiveness.We introduce new clustering criteria that treat utility and privacy on equal terms and propose sampling-based techniques to optimally set up its parameters.Extensive experiments show that the extended method achieves good accuracy in query answering and is able to prevent linking attacks effectively.