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Non-negative tensor factorization (NTF) has been successfully used to extract significant characteristics from polyadic data, such as data in social networks. Because these polyadic data have multiple dimensions (e.g., the author, content, and timestamp of a blog post), NTF fits in naturally and extracts data characteristics jointly from different data dimensions. In the traditional NTF, all information comes from the observed data, and therefore, the end users have no control over the outcomes. However, in many applications very often, the end users have certain prior knowledge, such as the demographic information about individuals in a social network or a pre-constructed ontology on the contents and therefore prefer the data characteristics extracting by NTF being consistent with such prior knowledge. To allow users' prior knowledge to be naturally incorporated into NTF, in this paper, we present a general framework-FacetCube-that extends the standard NTF. The new framework allows the end users to control the factorization outputs at three different levels for each of the data dimensions. The proposed framework is intuitively appealing in that it has a close connection to the probabilistic generative models. In addition to introducing the framework, we provide an iterative algorithm for computing the optimal solution to the framework. We also develop an efficient implementation of the algorithm that consists of several techniques to make our framework scalable to large data sets. Extensive experimental studies on a paper citation data set and a blog data set demonstrate that our new framework is able to effectively incorporate users' prior knowledge, improves performance over the traditional NTF on the task of personalized recommendation, and is scalable to large data sets from real-life applications. [ABSTRACT FROM AUTHOR]

Maximum correntropy criterion (MCC) based adaptive filters are found to be robust against impulsive interference. This paper proposes a novel MCC based adaptive filter with variable step size in order to obtain improved performance in terms of both convergence rate and steady state error with robustness against impulsive interference. The optimal variable step size is obtained by minimizing the Mean Square Deviation (MSD) error from one iteration to the other. Simulation results in the context of a highly impulsive system identification scenario show that the proposed algorithm has faster convergence and lesser steady state error than the conventional MCC based adaptive filters. [ABSTRACT FROM AUTHOR]

The ongoing exponential growth of online information sources has led to a need for reliable and efficient algorithms for text clustering. In this paper, we propose a novel text model called the relational text model that represents each sentence as a binary multirelation over a concept space \documentclass{article}\usepackage{amssymb}\pagestyle{empty}\begin{document}${\mathcal{C}}$\end{document}. Through usage of the smart indexing engine (SIE), a patented technology of the Belgian company i.Know, the concept space adopted by the text model can be constructed dynamically. This means that there is no need for an a priori knowledge base such as an ontology, which makes our approach context independent. The concepts resulting from SIE possess the property that frequency of concepts is a measure for relevance. We exploit this property with the development of the CR-algorithm. Our approach relies on the representation of a data set \documentclass{article}\usepackage{amssymb}\pagestyle{empty}\begin{document}${\mathcal{D}}$\end{document} as a multirelation, of which k-cuts can be taken. These cuts can be seen as sets of relevant patterns with respect to the topics that are described by documents. Analysis of dependencies between patterns allows to produce clusters, such that precision is sufficiently high. The best k-cut is the one that best approximates the estimated number of clusters to ensure recall. Experimental results on Dutch news fragments show that our approach outperforms both basic and advanced methods. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

This paper describes an approach to detecting distributed denial of service (DDoS) attacks that is based on fundamentals of Information Theory, specifically Kolmogorov Complexity. A theorem derived using principles of Kolmogorov Complexity states that the joint complexity measure of random strings is lower than the sum of the complexities of the individual strings when the strings exhibit some correlation. Furthermore, the joint complexity measure varies inversely with the amount of correlation. We propose a distributed active network-based algorithm that exploits this property to correlate arbitrary traffic flows in the network to detect possible denial-of-service attacks. One of the strengths of this algorithm is that it does not require special filtering rules and hence it can be used to detect any type of DDoS attack. We implement and investigate the performance of the algorithm in an active network. Our results show that DDoS attacks can be detected in a manner that is not sensitive to legitimate background traffic. [ABSTRACT FROM AUTHOR]

This paper concerns a new approach to intelligent data analysis based on information flow distribution study in a flow graph. Branches of a flow graph are interpreted as decision rules, whereas a flow graph is supposed to describe a decision algorithm. We propose to model decision processes as flow graphs and analyze decisions in terms of flow spreading in a graph. [ABSTRACT FROM AUTHOR]

A new model for divisible load problem is introduced. Its characteristics are analyzed. Optimal load distribution algorithms on the new model are presented for the tree-network and linear network. Applications that fit our model are briefly described. We show that our model outperforms the existing model such as Cheng–Robertazzi model. We show that the linear model is equivalent to a single-level tree network if the intermediate processors do not follow the store-and-forward communication model, but they follow the store-and-bypass model. This paper introduces the concept of store-and-bypass for divisible load theory. [ABSTRACT FROM AUTHOR]

The ranking of Atanassov's intuitionistic fuzzy values (A-IFVs) is nontrivial because there is no natural linear order among them, as opposed to fuzzy sets. In this paper, we tackle this difficult problem and develop a new novel technique for ranking A-IFVs based on amount of information using the geometrical representation of Atanassov's intuitionistic fuzzy sets (A-IFSs), with the aim of overcoming some drawbacks and ambiguities in existing methods and to build a new ranking model in the A-IFS context from the viewpoint of amount of information. We prove that the order on nonempty A-IFVs induced by our function is admissible in the A-IFS context. Thus, our developed method provides a total order that extends the usual partial order between A-IFVs. Moreover, we discuss the role that a decision maker's attitude can play in decision making under uncertainty. This allows us to introduce the idea of attitudinal-based extension of our developed technique, which is much more useful and flexible in real-world applications. By this, we also provide a new idea and explanation to evaluate two special A-IFVs: the completely known opposition (0,1,0) and the completely unknown information (0,0,1). Finally, an effort is made to extend the applicability of the developed methods by considering the situation in which Atanassov's interval-valued intuitionistic fuzzy sets are involved. Experimental studies show the good performance of the developed methods in the A-IF and A-IVIF situations, respectively. [ABSTRACT FROM AUTHOR]

Krawtchouk polynomials (KPs) and their moments are promising techniques for applications of information theory, coding theory, and signal processing. This is due to the special capabilities of KPs in feature extraction and classification processes. The main challenge in existing KPs recurrence algorithms is that of numerical errors, which occur during the computation of the coefficients in large polynomial sizes, particularly when the KP parameter (p) values deviate away from 0.5 to 0 and 1. To this end, this paper proposes a new recurrence relation in order to compute the coefficients of KPs in high orders. In particular, this paper discusses the development of a new algorithm and presents a new mathematical model for computing the initial value of the KP parameter. In addition, a new diagonal recurrence relation is introduced and used in the proposed algorithm. The diagonal recurrence algorithm was derived from the existing n direction and x direction recurrence algorithms. The diagonal and existing recurrence algorithms were subsequently exploited to compute the KP coefficients. First, the KP coefficients were computed for one partition after dividing the KP plane into four. To compute the KP coefficients in the other partitions, the symmetry relations were exploited. The performance evaluation of the proposed recurrence algorithm was determined through different comparisons which were carried out in state-of-the-art works in terms of reconstruction error, polynomial size, and computation cost. The obtained results indicate that the proposed algorithm is reliable and computes lesser coefficients when compared to the existing algorithms across wide ranges of parameter values of p and polynomial sizes N. The results also show that the improvement ratio of the computed coefficients ranges from 18.64% to 81.55% in comparison to the existing algorithms. Besides this, the proposed algorithm can generate polynomials of an order ∼8.5 times larger than those generated using state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we prove the following two results that expose some combinatorial limitations to list decoding Given n distinct elements α1,…,αn, from a field F, and n subsets S1,…Sn of F each of size at most l, the list decoding algorithm of Guruswami and Sudan can in polynomial time output all polynomials p of degree at most k that satisfy p(αi] ϵ Si for every i, as long as l ≤ [n⁄k]. We show that the performance of this algorithm is the best possible in a strong sense; specifically, when l = [n⁄k], the list of output polynomials can be superpolynomially large in n. For Reed-Solomon codes of block length n and dimension k + 1 where k = nδ for small enough δ, we exhibit an explicit received word with a superpolynomial number of Reed-Solomon codewords that agree with it on (2 - ϵ)k locations, for any desired ϵ ≥ 0 (agreement of k is trivial to achieve). Such a bound was known earlier only for a nonexplicit center. Finding explicit bad list decoding configurations is of significant interest—for example, the best known rate versus distance tradeoff, due to Xing, is based on a bad list decoding configuration for algebraic-geometric codes, which is unfortunately not explicitly known. [ABSTRACT FROM AUTHOR]

An important goal of indoor location estimation systems is to increase the estimation accuracy while reducing the power consumption. In this paper, we present a novel algorithm known as CaDet for power-efficient location estimation by intelligently selecting the number of Access Points (APs) used for location estimation. We show that by employing machine learning techniques, CaDet is able to use a small subset of the APs in the environment to detect a client's location with high accuracy. CaDet uses a combination of information theory, clustering analysis, and a decision tree algorithm. By collecting data and testing our algorithms in a realistic WLAN environment in the computer science department area of the Hong Kong University of Science and Technology, we show that CaDet (Clustering and Decision Tree-based method) can be much higher in accuracy as compared to other methods. We also show through experiments that, by intelligently selecting APs, we are able to save the power on the client device while achieving the same level of accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a novel system framework for interactive, three-dimensional, stylized, abstract painterly rendering. In this framework, the input models are first represented using 3D point sets and than this point-based representation is used to build a multiresolution bounding sphere hierarchy. From the leaf to root nodes, spheres of various sizes are rendered into multiple-size strokes on the canvas. The proposed sphere hierarchy is developed using multiscale region segmentation. This segmentation task assembles spheres with similar attribute regularities into a meaningful region hierarchy. These attributes include colors, positions, and curvatures. This hierarchy is very useful in the following respects: 1) it ensures the screen-space stroke density, 2) controls different input model abstractions, 3) maintains region structures such as the edges/boundaries at different scales, and 4) renders models interactively. By choosing suitable abstractions, brush stroke, and lighting parameters, we can interactively generate various painterly styles. We also propose a novel scheme that reduces the popping effect in animation sequences. Many different stylized images can be generated using the proposed framework. [ABSTRACT FROM AUTHOR]

In this paper, we extend our greedy network-growing algorithm to multi-layered networks. With multi-layered networks, we can solve many complex problems that single-layered networks fail to solve. In addition, the network-growing algorithm is used in conjunction with teacher-directed learning that produces appropriate outputs without computing errors between targets and outputs. Thus, the present algorithm is a very efficient network-growing algorithm. The new algorithm was applied to three problems: the famous vertical-horizontal lines detection problem, a medical data problem and a road classification problem. In all these cases, experimental results confirmed that the method could solve problems that single-layered networks failed to. In addition, information maximization makes it possible to extract salient features in input patterns. [ABSTRACT FROM AUTHOR]

In this paper, we examine the uniqueness (discrimination power) of a newly proposed graph invariant based on the matrix defined by Randić et al. In order to do so, we use exhaustively generated graphs instead of special graph classes such as trees only. Using these graph classes allow us to generalize the findings towards complex networks as they usually do not possess any structural constraints. We obtain that the uniqueness of this newly proposed graph invariant is approximately as low as the uniqueness of the Balaban index on exhaustively generated (general) graphs. [ABSTRACT FROM AUTHOR]

In ecological and behavioral research, drawing reliable conclusions from statistical models with multiple predictors is usually difficult if all predictors are simultaneously in the model. The traditional way of handling multiple predictors has been the use of threshold-based removal-introduction algorithms, that is, stepwise regression, which currently receives considerable criticism. A more recent and increasingly propagated modelling method for multiple predictors is the information theoretic (IT) approach that quantifies the relative suitability of multiple, potentially non-nested models based on a balance of model fit and the accuracy of estimates. Here, we examine three shortcomings of stepwise regression, subjective critical values, model uncertainty, and parameter estimation bias, which have been suggested to be avoided by applying information theory. We argue that, in certain circumstances, the IT approach may be sensitive to these issues as well. We point to areas where further testing and development could enhance the performance of IT methods and ultimately lead to robust inferences in behavioral ecology. [ABSTRACT FROM AUTHOR]

Modified Reconstructibility Analysis ( MRA ), a novel decomposition within the framework of set-theoretic (crisp possibilistic) reconstructibility analysis, is presented. It is shown that in some cases, while three-variable NPN-classified Boolean functions are not decomposable using Conventional Reconstructibility Analysis ( CRA ), they are decomposable using MRA. Also, it is shown that whenever a decomposition of three-variable NPN-classified Boolean functions exists in both MRA and CRA, MRA yields simpler or equal complexity decompositions. A comparison of the corresponding complexities for Ashenhurst-Curtis decompositions and MRA is also presented. While both AC and MRA decompose some but not all NPN-classes, MRA decomposes more classes, and consequently more Boolean functions. MRA for many-valued functions is also presented, and algorithms using two different methods (intersection and union) are given. A many-valued case is presented where CRA fails to decompose but MRA decomposes. [ABSTRACT FROM AUTHOR]

The inside-out sequential procedures for testing up to $$k$$ upper outliers in a two-parameter exponential sample are investigated. Six test statistics, one based on the ratio of the difference of largest observation and the sample mean which are unsuspected to be outliers to the range of these observations, and others used for block test procedures discussed in Basu (J Am Stat Assoc 60:548-559, ), Balasooriya and Gadag (J Stat Comput Simul 50:249-259, ), Zerbet and Nikulin (Commun Stat Theory Methods 32:573-583, ) and Kumar (Testing for suspected observations in an exponential sample with unknown origin, ), are considered. Utilizing the recursion of Huffer (J Appl Probab 25:346-354, ) and algorithm of Lin and Balakrishnan (Comput Stat Data Anal 53:3281-3290, ), the critical values of the joint null distributions of these test statistics for sequential testing discordancy of $$k$$ upper outliers in two-parameter exponential samples on the important cases $$k= 2$$ and $$3$$ are obtained. We also propose a simple procedure to determine $$k$$ , which can reduce the masking or swamping effect. Powers of tests based on these statistics are compared through a Monte Carlo study. [ABSTRACT FROM AUTHOR]

A brief review of the state-of-the-art in the control of free-flying space robots in the modes of manipulative target lockon by planning the gripper trajectory was presented. For the class of feedback systems, an alternative approach to this problem was proposed. Within the framework of this approach, consideration was given to the problems of constructing the working zone of the space manipulative robot and generating an algorithm to control the manipulative positioning of the load at the desired point of the inertial space with the availability of information about the direction to the target and the distance to it which is acquired using a camcorder mounted at the end link of the manipulator. Examples were presented of computer-aided construction of the working zone and modeling the space robot dynamics which corroborate operability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

We revisit the regular-expression matching problem with respect to prefix-freeness of the pattern. It is known that a prefix-free pattern gives only a linear number of matching substrings in the size of an input text. We improve the previous algorithm and suggest an efficient algorithm that finds all pairs (start, end) of start and end positions of all matching substrings with a single scan of the input when the pattern is a prefix-free regular expression. [ABSTRACT FROM AUTHOR]

Geometric feasibility (GF) of components is the precondition for assembly/disassembly sequence planning (ASP/DSP), and interference matrix is an important assembly relation model for GF discrimination. Extended interference matrix (EIM) is proposed to determine GF in the slanting directions, whose descriptive directions are extended to axes of a local coordinate system in each component. Turning interference matrix (TIM) is proposed to analyze the GF of the component that must be assembled with a multidirectional path, with the structure compact enough to describe the interference information from a turning point. The methods for acquiring EIM and TIM rapidly and automatically are proposed, including the stepping precise detection, the rough detection, and the accelerated detection based on bounding box. The measures for restraining static hard interference automatically are studied. An algorithm for discriminating GF comprehensively based on EIM and TIM is then presented. An ASP system 'AutoAssem' is developed, with two instances illustrating the effectiveness of each interference detection method. EIM and TIM are testified to enrich the variety and variability of assembly directions, which provide great universality to the GF determination of large-scale assemblies, together with an effective foundation to ASP/DSP algorithms. [ABSTRACT FROM AUTHOR]

Collaborative media such as wikis have become enormously successful venues for information creation. Articles accrue information through the asynchronous editing of users who arrive both seeking information and possibly able to contribute information. Most articles stabilize to high-quality, trusted sources of information representing the collective wisdom of all the users who edited the article. We propose a model for information growth which relies on two main observations: (i) as an article's quality improves, it attracts visitors at a faster rate (a rich-get-richer phenomenon); and, simultaneously, (ii) the chances that a new visitor will improve the article drops (there is only so much that can be said about a particular topic). Our model is able to reproduce many features of the edit dynamics observed on Wikipedia; in particular, it captures the observed rise in the edit rate, followed by 1/t decay. Despite differences in the media, we also document similar features in the comment rates for a segment of the LiveJournal blogosphere. [ABSTRACT FROM AUTHOR]

In order to react to volatile market demand and uncertain production objectives, distributed manufacturing resources have to be dynamically configured. However, the dynamics of process planning and shop floor information makes shop floor reconfiguration a challenging problem. The shortcomings of the existing research on shop floor reconfiguration are first discussed. Then a multi-agent-based framework for dynamic shop floor reconfiguration is presented, and a mathematical programming model taking process planning into consideration is constructed as well. To coordinate the resource assignment among agents, a cooperative coevolutionary algorithm is also put forward to find the optimal solution of the reconfiguration model. The advantages of the proposed model are using combined multi-agent and mathematical programming method to decompose and optimize the problem of reconfiguration and considering alternative process plans. Furthermore, a prototype system for dynamic shop reconfiguration is developed. The results of this research will help solve the problem of shop floor reconfiguration with complex and dynamic interactive structure. [ABSTRACT FROM AUTHOR]

A simple on-line procedure is considered for the prediction of a real-valued sequence. The algorithm is based on a combination of several simple predictors. If the sequence is a realization of an unbounded stationary and ergodic random process then the average of squared errors converges, almost surely, to that of the optimum, given by the Bayes predictor. An analog result is offered for the classification of binary processes. [ABSTRACT FROM AUTHOR]

Rissanen provided a sequential universal coding algorithm based on a block partitioning scheme, where the source model is estimated at the beginning of each block. This approach asymptotically approaches the entropy at the fastest possible rate of ½ log(n) bits per unknown parameter. We show that the complexity of this algorithm is Ω(n log (n)), which is comparable to existing sequential universal algorithms. We provide a sequential O(n log (log (n))) algorithm by modifying Rissanen's block partitioning scheme. The redundancy with our approach is greater than with Rissanen's block partitioning scheme by a multiplicative factor 1 + O(1/log (log (n))), hence it asymptotically approaches the entropy at the fastest possible rate. [ABSTRACT FROM AUTHOR]

Cognitive models focus on information and the computational manipulation of information. Rational models optimize the function that relates the input of a process to the output. In contrast, efficient algorithms minimize the computational cost of processing in terms of time. Minimizing time is a better criterion for normative models, because it reflects the energy costs of a physical system. [ABSTRACT FROM AUTHOR]