Reduction In Cardinality Research Articles

Scientific Elegance in NIDS: Unveiling Cardinality Reduction, Box-Cox Transformation, and ADASYN for Enhanced Intrusion Detection

Scientific Elegance in NIDS: Unveiling Cardinality Reduction, Box-Cox Transformation, and ADASYN for Enhanced Intrusion Detection

Cardinality Reduction Theorem for Logics QHC and QH4

Cardinality Reduction Theorem for Logics QHC and QH4

Formula omitted]-means clustering for the aggregation of HFLTS possibility distributions: [formula omitted]-two-stage algorithmic paradigm

The use of the hesitant fuzzy linguistic term sets (HFLTSs) has recently become an important trend in fuzzy decision making, and aggregating HFLTSs and their extensions has now become crucial for making decisions. Previous approaches to aggregating possibility distributions for HFLTSs were based on the paradigm of computing with words, whereas few proposals have been made to aggregate HFLTS possibility distributions under the framework of statistical data analysis so as to reduce information loss and distortion. An initial attempt was the similarity-measure-based agglomerative hierarchical clustering (SM-AggHC) two-stage aggregation paradigm for HFLTS possibility distributions, which, however, presents some important performance limitations from time complexity and memory requirement perspectives. Thereby, this paper introduces a new approach, so called, “N-two-stage algorithmic aggregation paradigm driven by the K-means clustering” (N2S-KMC) to overcome these limitations by cardinality reduction in the first stage of the aggregation process. The subsequent stage uses the similarity-measure-based K-means clustering algorithm to outperform the SM-AggHC algorithm. Such an outperformance, from run time and memory usage, is demonstrated by experimental results.

Privacy on the Edge: Customizable Privacy-Preserving Context Sharing in Hierarchical Edge Computing

The booming of edge computing enables and reshapes this big data era. However, privacy issues arise because increasing volume of data are published per second while the edge devices can only provide limited computing and storage resources. In addition, this has been aggravated by new emerging features of edge computing, such as decentralized and hierarchical infrastructure, mobility, and content-aware applications. Although some existing privacy preserving methods are extended to this domain, the privacy issues of data dissemination between multiple edge nodes and end users is barely studied. Motivated by this, we propose a dynamic customizable privacy-preserving model based on Markov decision process to obtain the optimized trade-off between customizable privacy protection and data utility. We start with establishing a game model between users and adversaries based on a QoS-based payoff function. A modified reinforcement learning algorithm is deployed to derive the exclusive Nash Equilibrium. Furthermore, the model can achieve fast convergence by the reduction of cardinality from $n$ to 2. Extensive experimental results confirm the significance of the proposed model comparing to the existing work both in terms of effectiveness and feasibility.

A multi-breakpoints approach for symbolic discretization of time series

Time series discretization is a technique commonly used to tackle time series classification problems. This manuscript presents an enhanced multi-objective approach for the symbolic discretization of time series called eMODiTS. The method proposed uses a different breakpoints vector, defined per each word segment, to increase the search space of the discretization schemes. eMODiTS’ search mechanism is the well-known evolutionary multi-objective algorithm NSGA-II, which finds a set of possible solutions according to entropy, complexity, and information loss estimations. Final solutions were appraised depending on the misclassification rate computed through the decision tree classifier. The trees obtained also produce graphical and significant information from the regions, relationships, or patterns in each database. Our proposal was compared against ten state-of-the-art time symbolic discretization algorithms. The results suggest that our proposal finds a suitable discretization scheme regarding classification, dimensionality, cardinality reduction, and information loss.

A Hybrid Privacy Protection Scheme in Cyber-Physical Social Networks

The rapid proliferation of smart mobile devices has significantly enhanced the popularization of the cyber-physical social network, where users actively publish data with sensitive information. Adversaries can easily obtain these data and launch continuous attacks to breach privacy. However, existing works only focus on either location privacy or identity privacy with a static adversary. This results in privacy leakage and possible further damage. Motivated by this, we propose a hybrid privacy-preserving scheme, which considers both location and identity privacy against a dynamic adversary. We study the privacy protection problem as the tradeoff between the users aiming at maximizing data utility with high-level privacy protection while adversaries possessing the opposite goal. We first establish a game-based Markov decision process model, in which the user and the adversary are regarded as two players in a dynamic multistage zero-sum game. To acquire the best strategy for users, we employ a modified state-action-reward-state-action reinforcement learning algorithm. Iteration times decrease because of cardinality reduction from $n$ to 2, which accelerates the convergence process. Our extensive experiments on real-world data sets demonstrate the efficiency and feasibility of the propose method.

A hybrid gene selection method for microarray recognition

DNA microarray data is expected to be a great help in the development of efficient diagnosis and tumor classification. However, due to the small number of instances compared to a large number of genes, many of the computational learning methods encounter difficulties to select the low subgroups. In order to select significant genes from the high dimensional data for tumor classification, nowadays, several researchers are exploring microarray data using various gene selection methods. However, there is no agreement between existing gene selection techniques that produce the relevant gene subsets by which it improves the classification accuracy. This motivates us to invent a new hybrid gene selection method which helps to eliminate the misleading genes and classify a disease correctly in less computational time. The proposed method composes of two-stage, in the first stage, EGS method using multi-layer approach and f-score approach is applied to filter the noisy and redundant genes from the dataset. In the second stage, adaptive genetic algorithm (AGA) work as a wrapper to identify significant genes subsets from the reduced datasets produced by EGS that can contribute to detect cancer or tumor. AGA algorithm uses the support vector machine (SVM) and Naïve Bayes (NB) classifier as a fitness function to select the highly discriminating genes and to maximize the classification accuracy. The experimental results show that the proposed framework provides additional support to a significant reduction of cardinality and outperforms the state-of-art gene selection methods regarding accuracy and an optimal number of genes.

Spatial and Temporal Wind Power Forecasting by Case-Based Reasoning Using Big-Data

The massive penetration of wind generators in electrical power systems asks for effective wind power forecasting tools, which should be high reliable, in order to mitigate the effects of the uncertain generation profiles, and fast enough to enhance power system operation. To address these two conflicting objectives, this paper advocates the role of knowledge discovery from big-data, by proposing the integration of adaptive Case Based Reasoning models, and cardinality reduction techniques based on Partial Least Squares Regression, and Principal Component Analysis. The main idea is to learn from a large database of historical climatic observations, how to solve the windforecasting problem, avoiding complex and time-consuming computations. To assess the benefits derived by the application of the proposed methodology in complex application scenarios, the experimental results obtained in a real case study will be presented and discussed.

Quantization of Eigen Subspace for Sparse Representation

We propose sparse Karhunen–Loeve Transform (SKLT) method to sparse eigen subspaces. The sparsity (cardinality reduction) is achieved through the pdf-optimized quantization of basis function (vector) set. It may be considered an extension of the simple and soft thresholding (ST) methods. The merit of the proposed framework for sparse representation is presented for auto-regressive order one, AR(1), discrete process and empirical correlation matrix of stock returns for NASDAQ-100 index. It is shown that SKLT is efficient to implement and outperforms several sparsity algorithms reported in the literature.

Parallel Sets Based on the Improved ACLEARCR Algorithm

Classic part-labeled data visualization method Parallel Sets is applied to represent visualization of multivariate data with measures. Currently, in all walks of life emerge abundant small records and documents. Thus to sort the massive categorical measures of small value on the variable axis plays an important role in reducing clutters in the view. This paper is based on the Advanced Categories Layout basEd on Average heuRistic with Cardinality Reduction (ACLEARCR), and proposes an optimization sequence algorithm aiming at categorical measures of small value. The case study proves that the proposed method is effective.

Compact optical CDMA passive optical network with differentiated services

In this paper the improved versions of quadratic congruence (QC) codes for cardinality enlargement are proposed for spectral-amplitude coding (SAC) optical code-division multiple-access (OCDMA) network. These codes can adopt two-code keying for performance enhancement and the interference can be eliminated in the decoder by our interference elimination scheme proposed previously. In addition, to mitigate the cardinality reduction in the two-code keying scheme, hybrid schemes using QC and/or stuffed QC (SQC) codes for differentiated service provision are proposed. These two code families are capable of using compact encoders and decoders for the application of passive optical networks.

Obtaining information from time data statistical analysis in human component system studies. (I). Methods and performances

Abstract This article states the problem of the time data exploration as the succession of the many analysis paths taken prior to obtaining results, results which are initially in a latent state. The initial data – obtained by an experiment or an observation design – are placed within a hyperparallelepiped HP0 where the directions correspond to factors established a priori and each cell contains a multidimensional signal. An analysis path is thus considered to be a progressive information transformation of the time data including up to five stages – data characterizing, scale transformation, data shaping, statistical analysis method application and result presentation. For each stage, a non-exhaustive set of methods is proposed. To assess the performance of each stage, several methods are suggested. More particularly, the evaluation of the first stage is considered either in terms of a cardinality reduction between the input and output hyperparallelepipeds HP0 and HP1 or in terms of distance comparisons between the cells of HP0 and HP1. A discussion of the main statistical behaviors encountered in the literature is included.