Ordered Subsets Research Articles

A multi-objective immune algorithm for intrusion feature selection

Feature selection plays a crucial role in classification problems, which tries to remove redundant or irrelevant features by mapping high-dimensional data to low-dimensional ones. Thus, this approach can improve the classification accuracy and reduce the computational cost to train the classification model. In this paper, we suggest an improved multi-objective immune algorithm (MOIA) for feature selection in intrusion detection. Specifically, the feature subsets for intrusion detection are treated as the individuals for immune optimization, which will select suitable feature subsets in order to reduce the dimensions of the dataset. After that, a neural network is used to train the classification model using the selected suitable feature subsets, and the output of the classification model is regarded as the target fitness value for each individual. As multiple attack types are considered in this paper, a traditional MOIA is modified by using an elite selection strategy based on the reference vectors, which can maintain the individuals with more promising performance when distinguishing more than five attack types in intrusion detection. By this way, the proposed algorithm can accelerate the convergence speed of classification, which also improves the classification accuracy. Experimental results on the NSL-KDD and UNSW-NB15 datasets validate the higher classification accuracy of the proposed algorithm.

Model-Based Deep Learning PET Image Reconstruction Using Forward-Backward Splitting Expectation-Maximization.

We propose a forward-backward splitting algorithm to integrate deep learning into maximum-a-posteriori (MAP) positron emission tomography (PET) image reconstruction. The MAP reconstruction is split into regularization, expectation-maximization (EM), and a weighted fusion. For regularization, the use of either a Bowsher prior (using Markov-random fields) or a residual learning unit (using convolutional-neural networks) were considered. For the latter, our proposed forward-backward splitting EM (FBSEM), accelerated with ordered subsets (OS), was unrolled into a recurrent-neural network in which network parameters (including regularization strength) are shared across all states and learned during PET reconstruction. Our network was trained and evaluated using PET-only (FBSEM-p) and PET-MR (FBSEM-pm) datasets for low-dose simulations and short-duration in-vivo brain imaging. It was compared to OSEM, Bowsher MAPEM, and a post-reconstruction U-Net denoising trained on the same PET-only (Unet-p) or PET-MR (Unet-pm) datasets. For simulations, FBSEM-p(m) and Unet-p(m) nets achieved a comparable performance, on average, 14.4% and 13.4% normalized root-mean square error (NRMSE), respectively; and both outperformed OSEM and MAPEM methods (with 20.7% and 17.7% NRMSE, respectively). For in-vivo datasets, FBSEM-p(m), Unet-p(m), MAPEM, and OSEM methods achieved average root-sum-of-squared errors of 3.9%, 5.7%, 5.9%, and 7.8% in different brain regions, respectively. In conclusion, the studied U-Net denoising method achieved a comparable performance to a representative implementation of the FBSEM net.

Symmetry-breaking inequalities for ILP with structured sub-symmetry

We consider integer linear programs whose solutions are binary matrices and whose (sub-)symmetry groups are symmetric groups acting on (sub-)columns. Such structured sub-symmetry groups arise in important classes of combinatorial problems, e.g. graph coloring or unit commitment. For a priori known (sub-)symmetries, we propose a framework to build (sub-)symmetry-breaking inequalities for such problems, by introducing one additional variable per considered sub-symmetry group. The derived inequalities are full-symmetry-breaking and in polynomial number w.r.t. the number of sub-symmetry groups considered. The proposed framework is applied to derive such inequalities when the symmetry group is the symmetric group acting on the columns. It is also applied to derive sub-symmetry-breaking inequalities for the graph coloring problem. Experimental results give insight into how to select the right inequality subset in order to efficiently break sub-symmetries. Finally, the framework is applied to derive (sub-)symmetry breaking inequalities for Min-up/min-down Unit Commitment Problem with or without ramp constraints. We show the effectiveness of the approach by presenting an experimental comparison with state-of-the-art symmetry-breaking formulations.

Automorphism groups and new constructions of maximum additive rank metric codes with restrictions

Let d,n∈Z+ such that 1≤d≤n. A d-codeC⊂Fqn×n is a subset of order n square matrices with the property that for all pairs of distinct elements in C, the rank of their difference is greater than or equal to d. A d-code with as many as possible elements is called a maximumd-code. The integer d is also called the minimum distance of the code. When d<n, a classical example of such an object is the so-called generalized Gabidulin code (Gabidulin and Kshevetskiy (2005)). In Delsarte and Goethals (1975), Schmidt (2015) and K.U. Schmidt (2018), several classes of maximum d-codes made up respectively of symmetric, alternating and Hermitian matrices were exhibited. In this article we focus on such examples.Precisely, we determine their automorphism groups and solve the equivalence issue for them. Finally, we exhibit a maximum symmetric 2-code which is not equivalent to the one with same parameters constructed in Schmidt (2015).

R-Ensembler: A greedy rough set based ensemble attribute selection algorithm with kNN imputation for classification of medical data

Background and ObjectiveRetrieving meaningful information from high dimensional dataset is an important and challenging task. Normally, medical dataset suffers from several issues such as curse of dimensionality problem, uncertainty, presence of missing values, non-relevant and redundant attributes, etc. Any machine learning technique applied on such data (without any preprocessing) by and large takes a considerable amount of computational time and may degrade the performance of the model. MethodsIn this article, R-Ensembler, a parameter free greedy ensemble attribute selection method is proposed adopting the concept of rough set theory by using the attribute-class, attribute-significance and attribute-attribute relevance measures to select a subset of attributes which are most relevant, significant and non-redundant from a pool of different attribute subsets in order to predict the presence or absence of different diseases in medical dataset. The main role of the proposed ensembler is to combine multiple subsets of attributes produced by different rough set filters and to produce an optimal subset of attributes for subsequent classification task. A novel n number of set intersection method is also proposed to reduce the biasness during the time of attribute selection process. Before selecting the minimal attribute set from a given data by the proposed R-Ensembler method, the dataset is preprocessed by the k nearest neighbour (kNN) imputation method for missing value treatment. ResultsExperiments are carried out on seven benchmark medical datasets collected from University of California at Irvine (UCI) repository. The performance of the proposed ensemble method is compared with five state-of-the-art attribute selection algorithms, results of which are measured using three benchmark classifiers viz., Naïve Bayes, decision trees and random forest. Experimental results clearly justify the superiority of the proposed R-Ensembler method over other attribute selection algorithms. Results of paired t-test performed on average accuracies produced by different classifiers simulated on the reduced data sets achieved by the proposed and counter part attribute selection methods confirm the statistical significance of the better reduced attribute subsets achieved by the proposed R-Ensembler method compared to others. ConclusionThe proposed ensemble method turned out to be very effective for selecting high relevant, high significant and less redundant attributes from a pool of different subsets of attributes.

Application of the experimental design algorithms to the construction of estimated subsets of minimum covariance determinant method

The problem of stable estimation of errors in variables models is considered in this paper. The basic concepts of experimental design methods, orthogonal regression method, the minimum covariance determinant method are investigated. Experiment planning methods use to form estimated subsets in order to achieve more accurate estimation results. Designs of experiments will allow one to receive the most informative points on which estimation will be made. Research, displaying change in the condition of the D-optimal design when applying different spectra of the design and in the conditions of various kinds of emissions, has been conducted in this work. According to the research it is found that the most accurate estimates are obtained when applying the methods of experiment planning.

Model-Based Deep Learning PET Image Reconstruction Using Forward-Backward Splitting Expectation Maximisation.

We propose a forward-backward splitting algorithm for maximum-a-posteriori (MAP) PET image reconstruction, which provides an elegant framework for integration of deep learning (DL) into model-based iterative reconstruction. The MAP reconstruction is split into three steps: regularisation, expectation maximisation (EM) and a weighted fusion. For regularisation, use of either a weighted Tikhonov prior (a hypothesis-driven prior based on Markov random fields) or a residual learning unit (a data-driven prior based on convolutional neural networks) were considered. For the latter, our proposed forward-backward splitting expectation maximisation (FBSEM), accelerated with ordered subsets (OS), was unrolled into a recurrent neural network in which the network parameters (including regularisation strength) are shared across all states and learned while PET images are reconstructed from emission data. FBSEM net was trained on simulated brain phantoms using PET-MRI (FBSEM-PM net) and PET-only (FBSEM-P net) data and was compared to OSEM, MR-guided MAPEM and a post-reconstruction U-Net denoiser, trained on the same PET dataset, for different count-levels. Our results showed that FBSEM-PM net accurately reconstructs test data without suppressing PET unique lesions or introducing MR unique lesions. It was also found that FBSEM-P net and U-Net both outperform MAPEM and OSEM reconstructions and achieve similar performance for moderate-to-high count levels. However, as the count level is reduced, the FBSEM-P net results in notably lower noise-induced hot/cold spots. These results therefore highlight the superiority of model-based DL reconstruction over conventional MAPEM and post-reconstruction DL-based denoising methods. Future work will include realistic 3D simulations and transfer learning for reconstruction of in-vivo PET and PET-MR data.

Order acceptance and scheduling with sequence-dependent setup times: A new memetic algorithm and benchmark of the state of the art

The Order Acceptance and Scheduling (OAS) problem describes a class of real-world problems such as in smart manufacturing and satellite scheduling. This problem consists of simultaneously selecting a subset of orders to be processed as well as determining the associated schedule. A common generalization includes sequence-dependent setup times and time windows. We propose a novel memetic algorithm for this problem, called Sparrow. It comprises a hybridization of biased random key genetic algorithm (BRKGA) and adaptive large neighbourhood search (ALNS). Sparrow integrates the exploration ability of BRKGA and the exploitation ability of ALNS. On a set of standard benchmark instances, this algorithm obtains better-quality solutions with runtimes comparable to state-of-the-art algorithms. To further understand the strengths and weaknesses of these algorithms, their performance is also compared on a set of new benchmark instances with more realistic properties. We conclude that Sparrow is distinguished by its ability to solve difficult instances from the OAS literature, and that the hybrid steady-state genetic algorithm (HSSGA) performs well on large instances in terms of optimality gap, although taking more time than Sparrow.

Optimized neuro-PSO-based software maintainability prediction using relief features selection method

&lt;p&gt;The recent development in software engineering reveals the importance of software maintenance during the time of software development that is becoming more important in software development environment and software metrics, which are very essential for measuring the maintainability of software, software complexity, estimating size, quality and project efforts. There are various approaches through which one can estimate the software cost and predict on various kinds of deliverable items. This paper aims at developing an optimized Neuro-PSO-based software maintainability prediction model by applying the dimensionality reduction using relief feature selection method for identifying the optimal feature subsets in order to increase the accuracy and reduce the time complexity of the prediction model. The simulation result proves the performance of the proposed model which will be more beneficial for the software developers in predicting the maintenance of the software in advance.&lt;/p&gt;

Species‐pair associations, null models, and tests of mechanisms structuring ecological communities

AbstractBiotic interactions and niche processes are fundamental determinants of community structure and species co‐occurrence. Most studies of species co‐occurrence have focused on negative association patterns (segregation presumed to arise from competition), often ignoring positive aggregations, although positive and negative associations may arise from multiple mechanisms. We used a pairwise approach to identify co‐occurrence patterns of 76 fish species across almost 9500 lakes, followed by a meta‐analytic approach to compare the co‐occurrence pattern of each species pair across watersheds and determine their cumulative species associations. Biological information relating to species’ phylogeny, habitat preferences, and diet was used to group species into relevant subsets in order to test community assembly processes of competition, predation, and habitat filtering. We found consistent non‐random patterns of co‐occurrence in nearly half the species pairs and more extremely aggregated than segregated species pairs. Observed co‐occurrence patterns indicated the importance of shared habitat requirements and predation, rather than competition, driving positively aggregated and negatively segregated species associations, respectively. Our meta‐analytic approach incorporating important biological attributes permitted the testing of specific mechanisms of community assembly, providing novel insights into the major determinants of fish community structure and their generality across a vast set of lakes.

Insects Visiting Drippy Blight Diseased Red Oak Trees Are Contaminated with the Pathogenic Bacterium Lonsdalea quercina.

The focus of investigation in this study was to consider the potential of arthropods in the dissemination of the bacterium involved in drippy blight disease, Lonsdalea quercina. Arthropod specimens were collected and tested for the presence of the bacterium with molecular markers. The bacterium L. quercina was confirmed on 12 different insect samples from three orders (Coleoptera, Hemiptera, and Hymenoptera) and eight families (Buprestidae, Coccinellidae, Dermestidae, Coreidae, Pentatomidae and/or Miridae, Apidae, Formicidae, and Vespidae). Approximately half of the insects found to carry the bacterium were in the order Hymenoptera. Estimates of the insects that are contaminated with the bacterium, and likely carry it between trees, is conservative because the documented insects represent only a subset of the insect orders that were observed feeding on the bacterium or present on diseased trees yet were not able to be tested. The insects contaminated with L. quercina exhibited diverse life histories, where some had a facultative relationship with the bacterium and others sought it out as a food source. These findings demonstrate that a diverse set of insects naturally occur on diseased trees and may disseminate L. quercina.

Optimal designs of analogue and digital fractional order filters for signal processing applications

Our initial research work was focussed on employing metaheuristic optimization techniques to design optimal digital signal processing (DSP) systems such as the full band, conventional infinite impulse response differentiators and integrators meeting the accurate magnitude responses with a smaller average group delay. Since, integer order systems are a tight subset of the fractional order (FO) systems the research has been extended towards the optimal design of FO differentiators and integrators in the discrete domain with improved frequency response performances. Specific emphasis was laid on the feasibility of the designed FO differentiators in controlling a double-integrator plant. Analogue Butterworth filter with fractional stepping in the transition band has also been realized using an optimal integer-order rational transfer function. In future, we intend to design and implement generalized fractional-order filters on FPGA/DSP kit and FPAAs.

Correlation Based Feature Selection Algorithms for Varying Datasets of Different Dimensionality

Curse of dimensionality problem needs to be addressed carefully when designing a classifier. Given a huge dimensional dataset, one interesting problem is the choice of optimal selection of features for classification. Feature selection is an interesting and most optimal solution to the curse of dimensionality problem. Numerous feature selection algorithms have been proposed in the recent past to solve the curse of dimensionality problem but no one stop solution prevails. This paper proposes two novel algorithms for feature selection namely Reverse Piece-wise Correlation Based Feature Selection (RPwCBFS) and Shuffled Piece-wise Correlation Based Feature Selection (SPwCBFS) that divides the feature space into pieces and computes the similarity of feature subsets in reverse order and in random shuffled manner respectively. The proposed algorithms are compared with Fast Correlation Based Feature selection (FCBF), Fast Correlation Based Feature selection # (FCBF#) and Fast Correlation Based Feature selection In Piece (FCBFiP). Standard medium and huge dimensional datasets are used for experimentation purpose. Experimental results prove that the Reverse Piece-wise Correlation Based Feature Selection algorithm (RPwCBFS) and Shuffled Piece-wise Correlation Based Feature Selection algorithm (SPwCBFS) are prominent solution for feature selection when the underlying dataset is medium sized. For huge dimensional datasets, Shuffled Piece-wise Correlation Based Feature Selection algorithm (SPwCBFS) proves to be an optimal choice.

MoDeSuS: A Machine Learning Tool for Selection of Molecular Descriptors in QSAR Studies Applied to Molecular Informatics.

The selection of the most relevant molecular descriptors to describe a target variable in the context of QSAR (Quantitative Structure-Activity Relationship) modelling is a challenging combinatorial optimization problem. In this paper, a novel software tool for addressing this task in the context of regression and classification modelling is presented. The methodology that implements the tool is organized into two phases. The first phase uses a multiobjective evolutionary technique to perform the selection of subsets of descriptors. The second phase performs an external validation of the chosen descriptors subsets in order to improve reliability. The tool functionalities have been illustrated through a case study for the estimation of the ready biodegradation property as an example of classification QSAR modelling. The results obtained show the usefulness and potential of this novel software tool that aims to reduce the time and costs of development in the drug discovery process.

Time of flight PET reconstruction using nonuniform update for regional recovery uniformity.

Time of flight (TOF) PET reconstruction is well known to statistically improve the image quality compared to non-TOF PET. Although TOF PET can improve the overall signal to noise ratio (SNR) of the image compared to non-TOF PET, the SNR disparity between separate regions in the reconstructed image using TOF data becomes higher than that using non-TOF data. Using the conventional ordered subset expectation maximization (OS-EM) method, the SNR in the low activity regions becomes significantly lower than in the high activity regions due to the different photon statistics of TOF bins. A uniform recovery across different SNR regions is preferred if it can yield an overall good image quality within small number of iterations in practice. To allow more uniform recovery of regions, a spatially variant update is necessary for different SNR regions. This paper focuses on designing a spatially variant step size and proposes a TOF-PET reconstruction method that uses a nonuniform separable quadratic surrogates (NUSQS) algorithm, providing a straightforward control of spatially variant step size. To control the noise, a spatially invariant quadratic regularization is incorporated, which by itself does not theoretically affect the recovery uniformity. The Nesterov's momentum method with ordered subsets (OS) is also used to accelerate the reconstruction speed. To evaluate the proposed method, an XCAT simulation phantom and clinical data from a pancreas cancer patient with full (ground truth) and 6× downsampled counts were used, where a Poisson thinning process was employed for downsampling. We selected tumor and cold regions of interest (ROIs) and compared the proposed method with the TOF-based conventional OS-EM and OS-SQS algorithms with an early stopping criterion. In computer simulation, without regularization, hot regions of OS-EM and OS-NUSQS converged similarly, but cold region of OS-EM was noisier than OS-NUSQS after 24 iterations. With regularization, although the overall speeds of OS-EM and OS-NUSQS were similar, recovery ratios of hot and cold regions reconstructed by the OS-NUSQS were more uniform compared to those of the conventional OS-SQS and OS-EM. The OS-NUSQS with Nesterov's momentum converged faster than others while preserving the uniform recovery. In the clinical example, we demonstrated that the OS-NUSQS with Nesterov's momentum provides more uniform recovery ratios of hot and cold ROIs compared to the OS-SQS and OS-EM. Although the cost function of all methods is equivalent, the proposed method has higher structural similarity (SSIM) values of hot and cold regions compared to other methods after 24 iterations. Furthermore, our computing time using graphics processing unit was 80× shorter than the time using quad-core CPUs. This paper proposes a TOF PET reconstruction method using the OS-NUSQS with Nesterov's momentum for uniform recovery of different SNR regions. In particular, the spatially nonuniform step size in the proposed method provides uniform recovery ratios of different SNR regions, and the Nesterov's momentum further accelerates overall convergence while preserving uniform recovery. The computer simulation and clinical example demonstrate that the proposed method converges uniformly across ROIs. In addition, tumor contrast and SSIM of the proposed method were higher than those of the conventional OS-EM and OS-SQS in early iterations.

Methods to Study Monocyte and Macrophage Trafficking in Atherosclerosis Progression and Resolution.

Monocytes are circulating cells imperative to the response against pathogens. Upon infection, they are quickly recruited to the affected tissue where they can differentiate into specialized phagocytes and antigen-presenting cells. Additionally, monocytes play a vital role in chronic inflammation, where they can promote and enhance inflammation or induce its resolution. There are two major subsets of monocytes, "inflammatory" and "nonclassical," which are believed to have distinct functions. In atherosclerosis, both types of monocytes are constantly recruited to lesions, where they contribute to plaque formation and atherosclerosis progression. Surprisingly, these cells can also be recruited to lesions and promote resolution of atherosclerosis. Tracking these cells in various disease stages may inform about the dynamic changes occurring in the inflamed and resolving tissues. In this chapter we will discuss methods for differential labeling of the two monocyte subsetsin order to examine their dynamics in inflammation.

Monte Carlo-based SPECT reconstruction within the SIMIND framework

This paper presents the development and validation of a Monte Carlo-based singe photon emission computed tomography reconstruction program for parallel-hole collimation contained within the SIMIND Monte Carlo framework. The Monte Carlo code is used as an accurate forward-projector and is combined with a simplified back-projector to perform iterative tomographic reconstruction using the Maximum Likelihood Expectation Maximization and Ordered Subsets Expectation Maximization algorithms, together forming a program called SIMREC. The Monte Carlo simulation transforms the estimated source distribution directly from activity to counts in its projections. Hence, the reconstructed image is expressed in activity without reference to an external calibration. The program is tested using phantom measurements of spheres filled with 99mTc, 177Lu and 131I placed in air and centrally and peripherally in a water-filled elliptical phantom. The feasibility of applying the reconstruction to patients is also demonstrated for a range of radiopharmaceuticals. The deviation in total activity in the spheres ranged between −4.1% and 6.2% compared with the activity determined when preparing the phantom. The SIMREC program was found to be accurate with respect to activity estimation and to reconstruct visually acceptable images within a few hours when applied to patient examples.

The RMS survey: Ammonia mapping of the environment of young massive stellar objects – II★

We present the results from NH$_{3}$ mapping observations towards 34 regions identified by the Red MSX Source (RMS) survey. We have used the Australia Telescope Compact Array to map ammonia (1,1) and (2,2) inversion emission spectra at a resolution of 10'' with velocity channel resolution of 0.4$\,$km$\,$s$^{-1}$ towards the positions of embedded massive star formation. Complementary data have been used from the ATLASGAL and GLIMPSE Legacy Surveys in order to improve the understanding of the regions and to estimate physical parameters for the environments. The fields have typical masses of ~1000$\,$M$\odot$, radii of ~0.15$\,$pc and distances of ~3.5$\,$kpc. Luminosities range between ~10$^{3}$ to ~10$^{6}$$\,$L$\odot$ and kinetic temperatures between 10 and 40$\,$K. We classify each field into one of two subsets in order to construct an evolutionary system for massive star formation in these regions based on the morphology and relative positions of the NH$_{3}$ emission, RMS sources and ATLASGAL thermal dust emission. Differences in morphology between NH$_{3}$ emission and ATLASGAL clumps are shown to correspond to evolutionary stages of ongoing massive star formation in these regions. The study has been further refined by including the positions of known methanol and water masers in the regions to gain insight into possible protostellar regions and triggered star formation.

Equivalent key attack against a public‐key cryptosystem based on subset sum problem

The decisional version and computational version of the subset sum problem are known to be NP-complete and NP-hard. At International Symposium on Information Theory and its Applications 2012, Yasuyuki Murakami, Shinsuke Hamasho and Masao Kasahara presented a knapsack scheme based on the decisional version of the odd order subset sum problem. They claimed that the public sequence is indistinguishable from uniformly distributed sequences. In this study, the authors present an equivalent key attack against this scheme. More precisely, they firstly observe that there are many groups of equivalent keys, which satisfy several necessary conditions. Subsequently, they show that one can recover a group of equivalent keys by using the orthogonal lattice technique. The feasibility of the attack is validated by the experimental data when the bit length of secret keys is not too large. Hence, the security of the proposed scheme is overestimated.

Improving prediction of extracellular matrix proteins using evolutionary information via a grey system model and asymmetric under-sampling technique

Extracellular Matrix proteins (ECMP) play vigorous part in performing various biological functions including cell migration, adhesion, proliferation, differentiation. Furthermore, embryonic development, angiogenesis, gene expression, and tumor growth are also regulated by ECMP. In view of this incredible significance, precise and reliable identification of ECMP through computational techniques is highly requisite. Although, previous works made substantial improvement, however, accurately predicting ECMP from primary protein sequence is still at the infant stage due to the rapid growth of proteins samples in online databases. In the current study, a novel sequence-based prediction method called TargetECMP has been proposed, which is based on the evolutionary information extracted via a grey system model. It utilizes asymmetric under-sampling approach for splitting the benchmark dataset into eleven subsets in order to avoid class imbalance problem. Jackknife cross-validation test is performed with support vector machine (SVM) on each subset of data and then ensemble majority voting is utilized to integrate outputs of SVM against each subset. The experimental results achieved by TargetECMP outperformed the existing predictor on both benchmark dataset and independent dataset. Owning to best prediction results provided by TargetECMP, it is demonstrated that the analysis will provide novel insights into basic research, drug discovery and academia in general and function of extracellular matrix proteins in particular.