Set Membership Research Articles

EXamine: Visualizing annotated networks in Cytoscape

eXamine is a Cytoscape app that displays set membership as contours on top of a node-link layout of a small graph.In addition to facilitating interpretation of enriched gene sets of small biological networks, eXamine can be used in other domains such as the visualization of communities in small social networks. eXamine was made available on the Cytoscape App Store in March 2014, has since registered more than 7,700 downloads, and has been highly rated by more than 25 users.In this paper, we present eXamine's new automation features that enable researchers to compose reproducible analysis workflows to generate visualizations of small, set-annotated graphs.

Parallel multiple pattern matching schemes based on cuckoo filter for deep packet inspection on graphics processing units

A large amount of data now being transferred through networks has made deep packet inspection (DPI) an essential part of security activities. Several DPI systems are developed based on Bloom filters to defend against malicious worm attacks through the Internet. These approaches have achieved significant performance. However, they do not permit deletion of items from the set of target patterns. This study proposes two multiple pattern matching schemes for DPI to exploit high parallelism capacity of graphics processing units (GPUs). Firstly, a GPU-based Cuckoo filter scheme is proposed by adopting a new approximate set membership, called Cuckoo filter, for parallel multiple pattern matching. The Cuckoo filter has many advantages over the Bloom filter such as higher insert performance, higher lookup throughput, less memory consumption, less false positive rate, and delete operation support. Secondly, an implementation of the GPU-based Cuckoo filter, called GPUshared-based Cuckoo filter is proposed. This scheme can efficiently distribute input string and pre-processing data in the hierarchical memory of GPUs to optimise the performance of the GPU-based Cuckoo filter scheme. Experiments show that the proposed schemes offer better performance than the previous approaches based on the Bloom filter.

Knowledge discovery and semantic learning in the framework of axiomatic fuzzy set theory

Axiomatic fuzzy set (AFS) theory facilitates a way on how to transform data into fuzzy sets (membership functions) and implement their fuzzy logic operations, which provides a flexible and powerful tool for representing human knowledge and emulate human recognition process. In recent years, AFS theory has received increasing interest. In this survey, we report the current developments of theoretical research and practical advances in the AFS theory. We first review some notion and foundations of the theory with an illustrative example, then, we focus on the various extensions of AFS theory for knowledge discovery, including clustering, classification, rough sets, formal concept analysis, and other learning tasks. Due to its unique characteristics of semantic representation, AFS theory has been applied in multiple domains, such as business intelligence, computer vision, financial analysis, and clinical data analysis. This survey provides a comprehensive view of these advances in AFS theory and its potential perspectives.This article is categorized under: Technologies > Computational Intelligence

Recursive least squares identification for piecewise affine Hammerstein models

PurposeThe purpose of this paper is to probe the recursive identification of piecewise affine Hammerstein models directly by using input-output data. To explain the identification process of a parametric piecewise affine nonlinear function, the authors prove that the inverse function corresponding to the given piecewise affine nonlinear function is also an equivalent piecewise affine form. Based on this equivalent property, during the detailed identification process with respect to piecewise affine function and linear dynamical system, three recursive least squares methods are proposed to identify those unknown parameters under the probabilistic description or bounded property of noise.Design/methodology/approachFirst, the basic recursive least squares method is used to identify those unknown parameters under the probabilistic description of noise. Second, multi-innovation recursive least squares method is proposed to improve the efficiency lacked in basic recursive least squares method. Third, to relax the strict probabilistic description on noise, the authors provide a projection algorithm with a dead zone in the presence of bounded noise and analyze its two properties.FindingsBased on complex mathematical derivation, the inverse function of a given piecewise affine nonlinear function is also an equivalent piecewise affine form. As the least squares method is suited under one condition that the considered noise may be a zero mean random signal, a projection algorithm with a dead zone in the presence of bounded noise can enhance the robustness in the parameter update equation.Originality/valueTo the best knowledge of the authors, this is the first attempt at identifying piecewise affine Hammerstein models, which combine a piecewise affine function and a linear dynamical system. In the presence of bounded noise, the modified recursive least squares methods are efficient in identifying two kinds of unknown parameters, so that the common set membership method can be replaced by the proposed methods.

Efficient and Adaptive Procurement Protocol with Purchasing Privacy

A procurement protocol is a protocol for a buyer to purchase digital goods at their prices from a vendor. A procurement protocol with privacy preservation can be achieved by priced oblivious transfer (POT). POT allows the buyer to obliviously procure items one by one. An adaptive POT protocol only consumes $O(1)$ O ( 1 ) communication cost in each transaction, where all items are committed and encrypted before transactions. However, we found that the state-of-the-art adaptive POT protocol proposed by Rial et al. is less practical and does not meet real-world needs. It has to restrict to the one-buyer setting where all items are encrypted associated with one buyer’s public key. For multiple buyers, the vendor must respectively encrypt all the same items for each buyer. Besides, it has to employ computationally expensive primitives such as zero-knowledge proof which imply inefficient computation operations. It is therefore unscalable and unsuitable in large-scale applications. In this paper, we propose an efficient adaptive priced oblivious transfer protocol to address the aforementioned problems. The proposed adaptive POT is built on top of a new cryptographic primitive, namely, adaptive set membership encryption (ASME). In our proposed protocol, all items are encrypted without the use of buyers’ public keys and hence they can be used for universal buyers. Our protocol significantly reduces the transaction cost compared to existing schemes. For example, the communication in each transaction costs only 6 group elements compared to at least 141 group elements in Rial et al.’s protocol. The implementation shows that our protocol is efficient in terms of bandwidth and computational cost.

Morton filters

Approximate set membership data structures (ASMDSs) are ubiquitous in computing. They trade a tunable, often small, error rate ( ϵ ) for large space savings. The canonical ASMDS is the Bloom filter, which supports lookups and insertions but not deletions in its simplest form. Cuckoo filters (CFs), a recently proposed class of ASMDSs, add deletion support and often use fewer bits per item for equal ϵ . This work introduces the Morton filter (MF), a novel AS-MDS that introduces several key improvements to CFs. Like CFs, MFs support lookups, insertions, and deletions, but improve their respective throughputs by 1.3x to 2.5x, 0.9x to 15.5x, and 1.3x to 1.6x. MFs achieve these improvements by (1) introducing a compressed format that permits a logically sparse filter to be stored compactly in memory, (2) leveraging succinct embedded metadata to prune unnecessary memory accesses, and (3) heavily biasing insertions to use a single hash function. With these optimizations, lookups, insertions, and deletions often only require accessing a single hardware cache line from the filter. These improvements are not at a loss in space efficiency, as MFs typically use comparable to slightly less space than CFs for the same epsis; .

EXamine: Visualizing annotated networks in Cytoscape.

eXamine is a Cytoscape app that displays set membership as contours on top of a node-link layout of a small graph.In addition to facilitating interpretation of enriched gene sets of small biological networks, eXamine can be used in other domains such as the visualization of communities in small social networks. eXamine was made available on the Cytoscape App Store in March 2014, has since registered more than 7,200 downloads, and has been highly rated by more than 25 users.In this paper, we present eXamine's new automation features that enable researchers to compose reproducible analysis workflows to generate visualizations of small, set-annotated graphs.

Set-Membership Position Estimation With GNSS Pseudorange Error Mitigation Using Lane-Boundary Measurements

Model-based positioning methods involve nonlinear equations as is the case when using satellite pseudoranges on global navigation satellite systems (GNSSs) and local measurements on road features. As these are nonlinear models, classical estimation methods cannot provide guaranteed position estimation and can converge to local optima, sometimes far away from the global optimum or the true value. Based on interval analysis, set inversion, and constraints propagation on real values provide a framework that guarantees to find the true position with a characterized confidence domain. This paper describes an error bounded set membership algorithm that computes the absolute position of a road vehicle by using raw GNNS pseudoranges, lane boundary measurements, and a 2D road network map as geometric constraints. The algorithm is based on set inversion using interval analysis, and bounds are set on the measurements by taking into account a chosen risk. The GNSS pseudoranges errors are modeled carefully, and road constraints are formalized to provide additional information in the data fusion process. The proposed algorithm, named lane boundary augmented set-membership GNSS positioning (LB-ASGP), provides a novel and inexpensive approach to improve position estimation performance for road vehicles guaranteeing the enclosure of the computed solution with high confidence. Results from simulations and field experiments show that the LB-ASGP significantly reduces GNSS errors in the direction perpendicular to the lane thanks to the lane boundary measurements.

Set value‐based dynamic model development for non‐linear manoeuvring target tracking problem in the presence of unknown but bounded disturbances

The stochastic models developed for the manoeuvring target tracking (MTT) problem do not exhibit good performance owing to the lack of sufficient information about uncertainties. Moreover, each of the MTT models can be used for limited target manoeuvres. An ‘unknown but bounded’ (UBB)-based non-linear dynamic model for modelling time-dependent acceleration and model uncertainties is proposed for addressing the aforementioned problems. In the UBB approach, uncertainties and target manoeuvre are modelled using information about their upper bounds only. The extended set membership filter can be used to estimate the target states and manoeuvre with this new dynamic model. In order to examine the stability of the proposed method, the input to state stability of the estimation error is analysed. Very good performance is achieved by the proposed dynamic model without precise information about the model uncertainties. The proposed MTT algorithm can be used to track both non-manoeuvring and manoeuvring targets. The theoretical development is verified using simulations and the results are compared with those of other MTT methods. These comparisons demonstrate that the performance of the proposed method is better.

High-dimensional vector semantics

In this paper we explore the “vector semantics” problem from the perspective of “almost orthogonal” property of high-dimensional random vectors. We show that this intriguing property can be used to “memorize” random vectors by simply adding them, and we provide an efficient probabilistic solution to the set membership problem. Also, we discuss several applications to word context vector embeddings, document sentences similarity, and spam filtering.

Application of TOPSIS Based on Vague Sets in Marketing Scheme of New Products

To deal with accurately solve the multi-attribute decision-making problems of marketing programs, the vague set theory applied to marketing, program evaluation and determine the index weight using entropy method. Then use each alternative method TOPSIS score, ultimately the ideal solution. The example demonstrates the effectiveness of this method. This method takes into account the use of Vague set membership and non-membership of both information and taking into account the objectivity of the index weight, thus providing a reasonable and effective evaluation method program.

Distributed sparse diffusion estimation based on set membership and affine projection algorithm

The wireless sensor networks (WSNs) is the result of evolution of wireless and networking technologies, micro-electromechanical systems, and micro-services. One important task which can be performed by nodes in WSNs is to find a common solution to a problem by using distributed processing. In this paper, we study the problem of distributed estimation, where a group of nodes are required to collectively estimate a sparse parameter vector of interest, and we solve it by an estimation algorithm based on the set membership (SM) and affine projection (AP) methods. At each iteration of the algorithm, in addition to the current data, the proposed algorithm uses data obtained from previous measurements to attain faster convergence rate. A method is also proposed to select the constraint bound for set membership such that the computational load is uniformly distributed among the nodes of network. Then the distributed estimation algorithm is analyzed and a closed form expression for the steady state mean square deviation (MSD) is developed. The performance of proposed method is assessed via computer simulations. The simulation results show that the proposed algorithm provides faster convergence rate and smaller steady state MSD value when compared to conventional methods such as diffusion least mean squares (LMS), distributed set membership normalized LMS (DSM-NLMS), and distributed APA. Moreover, it achieves lower computational load compared to the AP method. These advantages make the proposed method useful in sparse parameter vector estimation whenever the nodes have sufficient memory size.

A novel image edge detection algorithm based on neutrosophic set

This study proposes a novel method to detect edge on clear or noisy images.The proposed approach performs well on images without noise or with different levels of noise.Neutrosophic set is employed to deal with uncertain information on image edge detection. Neutrosophic set (NS) theory is a formal framework to study the origin, nature, and scope of the neutral state. In this paper, neutrosophic set is applied in image domain and a new directional α-mean operation is defined. Based on this operation, neutrosophic set is applied into image edge detection procedure. First, the image is transformed into NS domain, which is described by three membership sets: T, I and F. Then, a directional α-mean operation is employed to reduce the indeterminacy of the image. Finally, a neutrosophic edge detection algorithm (NSED) is proposed based on the neutrosophic set and its operation to detect edge. Experiments have been conducted using numerous artificial and real images. The results demonstrate the NSED can detect the edges effectively and accurately. Particularly, it can remove the noise effect and detect the edges on both the noise-free images and the images with different levels of noises.

An exact test for comparing a fixed quantitative property between gene sets.

A significant difference in the distribution of a feature between two gene sets can provide insight into function or regulation. This statistical setting differs from much of hypothesis testing theory because the genome is often considered to be effectively fixed, finite and entirely known in commonly studied organisms, such as human. The Mann-Whitney U test is commonly employed in this scenario despite the assumptions of the test not being met, leading to unreliable and generally underpowered results. Permutation tests are also commonly employed for this purpose, but are computationally burdensome and are not tractable for obtaining small P values or for multiple comparisons. We present an exact test for the null hypothesis that gene set membership is independent of the quantitative gene feature of interest. We derive an analytic expression for the randomization distribution of the median of the quantitative feature under the null hypothesis. Efficient implementation permits calculation of precise P values of arbitrary magnitude and makes thousands of simultaneous tests of transcriptome-sized gene sets computationally tractable. The flexibility of the hypothesis testing framework presented permits extension to a variety of related tests commonly found in genomics. The exact test is used to identify signatures of translation control and protein function in the human genome. The exact test presented here is implemented in R in the package kpmt available on CRAN. map2085@med.cornell.edu. Supplementary data are available at Bioinformatics online.

Understanding social capital in sports clubs: participation, duration and social trust

A central claim in Robert Putnam’s theory of social capital is that participation in voluntary organizations can foster social trust among members that are active and engaged. Based on this claim, this article examines how duration of membership and member participation impacts upon levels of social trust among sports club members. The results show that neither participation nor duration impacts on levels of social trust – a result found in survey studies conducted in Denmark and the Netherlands. Sophisticated participation measures and the inclusion of duration of membership set this study apart from previous studies on social capital. The fact that no correlation can be identified between participation and duration on one side and social trust on the other adds weight to the claim that, even though numerous studies find higher levels of social trust among members of voluntary organizations than among non-members, this cannot be attributed to member socialization.

Generalized Orthopair Fuzzy Sets

We note that orthopair fuzzy subsets are such that that their membership grades are pairs of values, from the unit interval, one indicating the degree of support for membership in the fuzzy set and the other support against membership. We discuss two examples, Atanassov's classic intuitionistic sets and a second kind of intuitionistic set called Pythagorean. We note that for classic intuitionistic sets the sum of the support for and against is bounded by one, while for the second kind, Pythagorean, the sum of the squares of the support for and against is bounded by one. Here we introduce a general class of these sets called q-rung orthopair fuzzy sets in which the sum of the ${\rm{q}}$ th power of the support for and the ${\rm{q}}$ th power of the support against is bonded by one. We note that as q increases the space of acceptable orthopairs increases and thus gives the user more freedom in expressing their belief about membership grade. We investigate various set operations as well as aggregation operations involving these types of sets.

Single Event Transient Tolerant Bloom Filter Implementations

Bloom filters have been used to reduce the delay in networking and computing applications when a set membership check is to be applied. Error sources can affect the behavior of Bloom filters resulting in a wrong outcome of this membership test and a possible effect in the system's output. Single event transients are a type of temporary errors altering the operation of combinational logic. A single event transient affecting the hash generation logic of a hardware-implemented Bloom filter can produce errors such as false negatives. This paper presents different approaches to build Bloom filters that are tolerant to single event transients occurring in the hash generation circuitry. They are compared to the use of traditional Modular Redundancy approaches. The results show that the new schemes can reduce significantly the circuit area needed to implement the Bloom filter.

On the analysis of Bloom filters

The Bloom filter is a simple random binary data structure which can be efficiently used for approximate set membership testing. When testing for membership of an object, the Bloom filter may give a false positive, whose probability is the main performance figure of the structure. We complete and extend the analysis of the Bloom filter available in the literature by means of the γ-transform approach. Known results are confirmed and new results are provided, including the variance of the number of bits set to 1 in the filter. We consider the choice of bits to be set to 1 when an object is inserted both with and without replacement, in what we call standard and classic Bloom filter, respectively. Simple iterative schemes for the computation of the false positive probability and a new non-iterative approximation, taking into account the variance of bits set to 1, are also provided.

Improved solutions for ill-conditioned problems involved in set-membership estimation for fault detection and isolation

Set-membership (SM) estimation implies that the computed solution sets are guaranteed to contain all the feasible estimates consistent with the bounds specified in the model. Two issues often involved in the solution of SM estimation problems and their application to engineering case studies are considered in this paper. The first one is the estimation of derivatives from noisy signals, which in a bounded uncertainty framework means obtaining an enclosure by lower and upper bounds. In this paper, we improve existing methods for enclosing derivatives using Higher-Order Sliding Modes (HOSM) differentiators combining filtering. Our approach turns the use of high order derivatives more efficiently especially when the signal to differentiate has slow dynamics. The second issue of interest is solving linear interval equation systems, which is often an ill-conditioned problem. This problem is reformulated as a Constraint Satisfaction Problem and solved by the combination of the constraint propagation Forward Backward algorithm and the SIVIA algorithm. The two proposed methods are tested on illustrative examples. The two methods are then used in a fault detection and isolation algorithm based on SM parameter estimation that is applied to detect abnormal parameter values in a biological case study.

TinySet—An Access Efficient Self Adjusting Bloom Filter Construction

Bloom filters are a very popular and efficient data structure for approximate set membership queries. However, Bloom filters have several key limitations as they require 44% more space than the lower bound, their operations access multiple memory words, and they do not support removals. This paper presents TinySet, an alternative Bloom filter construction that is more space efficient than Bloom filters for false positive rates smaller than 2.8%, accesses only a single memory word and partially supports removals. TinySet is mathematically analyzed and extensively tested and is shown to be fast and more space efficient than a variety of Bloom filter variants. TinySet also has low sensitivity to configuration parameters and is therefore more flexible than a Bloom filter.