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Decision models such as the drift-diffusion model (DDM) are widely used and broadly accepted for their ability to account remarkably well for binary choices between perceptual hypotheses about a stimulus or for preferential decisions between valued options (such as snacks or consumer products), and for their response time (RT) distributions, as a function of the stimulus or the option values. The DDM is built on an evidence accumulation to bound concept, where, in the value domain, a decision maker repeatedly samples the mental representations of the values of the options on offer until satisfied that there is enough evidence in favor of one option over the other. As the value signals that drive the DDM evidence are derived from value estimates that are not known to the decision maker with certainty, repeated sequential samples are necessary to average out noise. The classic DDM typically does not allow for different options to have different levels of variability in their value representations. However, recent value-based decision studies have shown that decision makers often report levels of certainty regarding value estimates that vary across choice options. There is therefore a need to extend the DDM to include an option-specific value certainty component. We present several such DDM extensions and validate them against empirical data from four previous studies. The data supports best a DDM version in which the drift of the accumulation is based on a sort of signal-to-noise ratio of value for each option (rather than a mere accumulation of samples from the corresponding value distributions). This DDM variant accounts for the positive impact of value certainty on choice consistency and for the negative impact of value certainty on RT.

Applying chaos theory for secure digital communications is promising and it is well acknowledged that in such applications the underlying chaotic systems should be carefully chosen. However, the requirements imposed on the chaotic systems are usually heuristic, without theoretic guarantee for the resultant communication scheme. Among all the primitives for secure communications, it is well-accepted that (pseudo) random numbers are most essential. Taking the well-studied two-dimensional coupled map lattice (2D CML) as an example, this paper performs a theoretical study towards pseudo-random number generation with the 2D CML. In so doing, an analytical expression of the Lyapunov exponent (LE) spectrum of the 2D CML is first derived. Using the LEs, one can configure system parameters to ensure the 2D CML only exhibits complex dynamic behavior, and then collect pseudo-random numbers from the system orbits. Moreover, based on the observation that least significant bit distributes more evenly in the (pseudo) random distribution, an extraction algorithm E is developed with the property that, when applied to the orbits of the 2D CML, it can squeeze uniform bits. In implementation, if fixed-point arithmetic is used in binary format with a precision of $z$ bits after the radix point, E can ensure that the deviation of the squeezed bits is bounded by $2^{-z}$ . Further simulation results demonstrate that the new method not only guide the 2D CML model to exhibit complex dynamic behavior, but also generate uniformly distributed independent bits. In particular, the squeezed pseudo random bits can pass both NIST 800-22 and TestU01 test suites in various settings. This study thereby provides a theoretical basis for effectively applying the 2D CML to secure communications., Comment: 10 pages, 11figures

In this paper, a new class of generalized cyclotomic binary sequences with period \begin{document}$ 4p^n $\end{document} is proposed. These sequences are almost balanced, and the explicit formulas of their linear complexity and autocorrelation are presented.

Subfield codes of linear codes over finite fields have recently received a lot of attention, as some of these codes are optimal and have applications in secrete sharing, authentication codes and association schemes. In this paper, a class of binary subfield codes is constructed from a special family of MDS codes, and their parameters are explicitly determined. The parameters of their dual codes are also studied. Some of the codes presented in this paper are optimal or almost optimal.

In this work, we define three composite matrices derived from group rings. We employ these composite matrices to create generator matrices of the form \begin{document}$ [I_n \ | \ \Omega(v)], $\end{document} where \begin{document}$ I_n $\end{document} is the identity matrix and \begin{document}$ \Omega(v) $\end{document} is a composite matrix and search for binary self-dual codes with parameters \begin{document}$ [36,18, 6 \ \text{or} \ 8]. $\end{document} We next lift these codes over the ring \begin{document}$ R_1 = \mathbb{F}_2+u\mathbb{F}_2 $\end{document} to obtain codes whose binary images are self-dual codes with parameters \begin{document}$ [72,36,12]. $\end{document} Many of these codes turn out to have weight enumerators with parameters that were not known in the literature before. In particular, we find \begin{document}$ 30 $\end{document} new Type I binary self-dual codes with parameters \begin{document}$ [72,36,12]. $\end{document}

Orbits and bi-invariant subsets of binary $G$-spaces are studied. The problem of the distributivity of a binary action of a group $G$ on a space $X$, which was posed in 2016 by one of the authors, is solved., 11 pages

The goal of supervised hashing is to construct hash mappings from collections of images and semantic annotations such that semantically relevant images are embedded nearby in the learned binary hash representations. Existing deep supervised hashing approaches that employ classification frameworks with a classification training objective for learning hash codes often encode class labels as one-hot or multi-hot vectors. We argue that such label encodings do not well reflect semantic relations among classes and instead, effective class label representations ought to be learned from data, which could provide more discriminative signals for hashing. In this article, we introduce Adaptive Labeling Deep Hashing (AdaLabelHash) that learns binary hash codes based on learnable class label representations. We treat the class labels as the vertices of a K -dimensional hypercube, which are trainable variables and adapted together with network weights during the backward network training procedure. The label representations, referred to as codewords, are the target outputs of hash mapping learning. In the label space, semantically relevant images are then expressed by the codewords that are nearby regarding Hamming distances, yielding compact and discriminative binary hash representations. Furthermore, we find that the learned label representations well reflect semantic relations. Our approach is easy to realize and can simultaneously construct both the label representations and the compact binary embeddings. Quantitative and qualitative evaluations on several popular benchmarks validate the superiority of AdaLabelHash in learning effective binary codes for image search.

Given a dataset $\cal S$ composed of g subsets with each data items belonging to one of them, $\textit{multiset membership lookup}$ takes an item e as input and outputs a binary answer whether $e\in{\cal S}$ and, in case of yes, the ID of the subset to which e belongs. Overlaid upon while more sophisticated than the canonical membership lookup, multiset membership lookup emerges as a pivotal functionality in many computing and networking paradigms. The quest to achieve high-speed, high-accuracy lookup with limited memory cost makes lookup algorithm design a challenging task, particularly when the data items arrive as a stream. In this paper, we devise compact data structures and lookup algorithms that are amendable for hardware implementation, while guaranteeing high lookup accuracy and supporting interactive query processing. We first propose $\textit{multi-hash color table}$ , a variant of Bloom filter, to encode subset IDs compactly and map the ID of an item to its subset ID. We further construct a more balanced data structure called $\textit{balanced multi-hash color table}$ to improve the compactness by integrating the state-of-the-art load balancing technique. We complete our work by addressing the case of $\textit{batch arrivals}$ and design a batched recording algorithm optimizing the memory efficiency.

Hash bit selection determines an optimal subset of hash bits from a candidate bit pool. It is formulated as a zero-one quadratic programming problem subject to binary and cardinality constraints. In this article, the problem is equivalently reformulated as a global optimization problem. A collaborative neurodynamic optimization (CNO) approach is applied to solve the problem by using a group of neurodynamic models initialized with particle swarm optimization iteratively in the CNO. Lévy mutation is used in the CNO to avoid premature convergence by ensuring initial state diversity. A theoretical proof is given to show that the CNO with the Lévy mutation operator is almost surely convergent to global optima. Experimental results are discussed to substantiate the efficacy and superiority of the CNO-based hash bit selection method to the existing methods on three benchmarks.

The solution behavior of a second element in the primary phase (α(Mg)) is important in the design of high-performance alloys. In this work, three sets of features have been collected: a) interaction features of solutes and Mg obtained from first-principles calculation, b) intrinsic physical properties of the pure elements and c) structural features. Based on the maximum solid solubility values, the solution behavior of elements in α(Mg) are classified into four types, e.g., miscible, soluble, sparingly-soluble and slightly-soluble. The machine learning approach, including random forest and decision tree algorithm methods, is performed and it has been found that four features, e.g., formation energy, electronegativity, non-bonded atomic radius, and work function, can together determine the classification of the solution behavior of an element in α(Mg). The mathematical correlations, as well as the physical relationships among the selected features have been analyzed. This model can also be applied to other systems following minor modifications of the defined features, if required.

Identification of the most prominent bio-markers or genes with high classification accuracy in the high-dimensional cancerous data, is still remained as an emerging challenge for the machine learning researchers. As this challenge has two objectives, i.e. minimizing the number of genes (NoG) and maximizing the classification accuracy percentage (CAP), this problem can be modeled as binary multi-objective approach. In this work, a modified version of multi-objective Jaya algorithm, multi-objective chaotic Jaya (MOCJaya), is suggested to select the minimum NoG with high CAP. Initially, a filter approach, namely Fisher score is applied to pre-select the informative genes. Then, MOCJaya algorithm is employed for both selecting key genes and classifying the cancer data. To assess the efficacy of the designed algorithm, ten binary and multi-class cancerous datasets are considered. Here, the suggested algorithm has been compared with multi-objective chaotic Genetic Algorithm (MOCGA), multi-objective chaotic particle swarm optimization (MOCPSO), multi-objective Jaya (MOJaya), multi-objective PSO (MOPSO), and non-dominated sorting GA (NSGA-II) models. Moreover, a comparison of MOCJaya algorithm with other seventeen existing models, is also performed here. The experimental results and comparison analysis reveal that MOCJaya classifies both the positive and negative samples of the cancer datasets in high CAP with a smaller NoG.

We consider the Pareto-scheduling of two competing agents on a single machine, in which the jobs of each agent have their “own equal processing times” (shortly, OEPT). In the literature, two special versions of the OEPT model, in which the jobs have either unit or equal processing times, have been well studied, where the criteria are given by various regular objective functions without including the late work criteria. However, for equal processing times, the exact complexity of three problems is still unaddressed. Two-agent scheduling related to late work criteria is also a hot topic in recent years. This inspires our research by also including the total (weighted) late work as criteria. We show that, for equal processing times, all the problems are binary NP-hard if the criterion of one agent is the total tardiness or the total late work and the criterion of the other agent is either the total tardiness or the total late work or the weighted number of tardy jobs or the total weighted completion time. As a result, complexity classification for equal processing times is completely addressed. We further show that all the problems under the OEPT model are either polynomially solvable or ordinary NP-hard, which results in a complete complexity classification for the OEPT model.

From the dawn of the computer age, man has mass produced binary components for computers, due to which ternary or higher radix computers are not yet commercially used. It has been proved that ternary logic can be more efficient than binary logic and there are many devices in development that operate on more than two internal states. Therefore an efficient method to produce multi-valued logic on the basis of binary input provided is needed. In this article, an effective, simple, flexible, and low power consumption implementation has been proposed that can convert any binary number to a number of chosen radix. The proposed design is implemented in 32 nm TSMC CMOS. The proposed design is then evaluated in power/delay space and its performances are compared with the latest state of art designs. The proposed circuit exhibits power saving up to 92 % over previous models.

Nowadays the majority of retail customers use multiple channels. We investigate the assortment, space and inventory problem for an omni-channel retailer operating with interconnected bricks-and-mortar stores and an online shop. For this problem it becomes essential to consider customers’ demand interactions across channels. Current literature mainly focuses on single-channel assortments and ignores cross-channel substitution. We contribute the first integrated omni-channel model that determines assortments for the online and bricks-and-mortar channel with stochastic, space-elastic and out-of-assortment and out-of-stock demand both for in-channel substitution and cross-channel substitution. A specialized heuristic is developed that is based on an iterative solution of a binary problem and demand updates. Our approach achieves near-optimal results for small instances and higher objective values as an alternative heuristic for larger instances. With the full integration of channels, omni-channel retailers can realize a profit increase that mainly depends on the magnitude of substitution rates. We further show numerically that in-channel substitution has a stronger impact on profits than cross-channel substitution when costs are equal across channels.

Boolean network is a modeling tool that describes a dynamic system with binary variables and their logical transition formulas. Recent studies in precision medicine use a Boolean network to discover critical genetic alterations that may lead to cancer or target genes for effective therapies to individuals. In this paper, we study a logical inference problem in a Boolean network to find all such critical genetic alterations in a minimal (parsimonious) way. We propose a bilevel integer programming model to find a single minimal genetic alteration. Using the bilevel integer programming model, we develop a branch and bound algorithm that effectively finds all of the minimal alterations. Through a computational study with eleven Boolean networks from the literature, we show that the proposed algorithm finds solutions much faster than the state-of-the-art algorithms in large data sets.

Job shops are an important production environment for low-volume high-variety manufacturing. Its scheduling has recently been formulated as an Integer Linear Programming (ILP) problem to take advantages of popular Mixed-Integer Linear Programming (MILP) methods, e.g., branch-and-cut. When considering a large number of parts, MILP methods may experience difficulties. To address this, a critical but much overlooked issue is formulation tightening. The idea is that if problem constraints can be transformed to directly delineate the problem convex hull in the data pre-processing stage, then a solution can be obtained by using linear programming methods without much difficulty. The tightening process, however, is NP hard because of the existence of integer variables. In this paper, an innovative and systematic approach is established for the first time to tighten the formulations of individual parts, each with multiple operations, in the data pre-processing stage. It is a major extension from our previous work on problems with binary and continuous variables to integer variables. The idea is to first link integer variables to binary variables by innovatively combining constraints so that the integer variables are uniquely determined by binary variables. With binary variables and continuous only, the vertices of the convex hull can be obtained based on the vertices of the linear problem after relaxing binary requirements with proved tightness. These vertices are then converted to tight constraints for general use. This approach significantly improves and extends our previous results on tightening single-operation parts without actually achieving tightness. Numerical results demonstrate significant benefits on solution quality and computational efficiency. This approach also applies to other ILP problems with similar characteristics and fundamentally changes the way how such problems are formulated and solved.

This paper presents a new optimization-based approach to determine the class of stabilizing control structures with the necessary set of feedback links for interconnected systems. The proposed approach relies on a graph theoretic interpretation and its equivalence in terms of binary linear programs (BLP). To carry out the primary goal, first, the stabilizability of an LTI system under the decentralized control structure is presented in terms of a BLP. Next, two graph-based criteria are proposed to characterize stabilizing control structures with the required feedback links. Finally, all possible stabilizing control structures with the necessary feedback links are derived via solving a set of BLPs. In addition to the analysis of stabilizing control structures, the proposed graph-theoretic approach offers a versatile set of tools to find the simplest control structures capable of assigning the closed-loop spectrum in a totally arbitrary desired region. Simulation results illustrate the assessment of stabilizing control structures, the required additional feedback links, and the elegant generalization of the proposed approach to regional pole-placement.

Devices that measure our physical, medical and mental condition have entered our daily life recently. Such devices measure our status in a continuous manner and can be useful in predicting future medical events or can guide us towards a healthier life. It is therefore important to establish that such devices record our behaviour in a reliable manner and measure what we believe they measure. In this article, we propose to measure the reliability and validity of a newly developed measuring device in time using a longitudinal model for sequential kappa statistics. We propose a Bayesian estimation procedure. The method is illustrated by a validation study of a new accelerometer in cardiopulmonary rehabilitation patients.

Feature selection is performed to eliminate irrelevant features to reduce computational overheads. Metaheuristic algorithms have become popular for the task of feature selection due to their effectiveness and flexibility. Hybridization of two or more such metaheuristics has become popular in solving optimization problems. In this paper, we propose a hybrid wrapper feature selection technique based on binary butterfly optimization algorithm (bBOA) and Simulated Annealing (SA). The SA is combined with the bBOA in a pipeline fashion such that the best solution obtained by the bBOA is passed on to the SA for further improvement. The SA solution improves the best solution obtained so far by searching in its neighborhood. Thus the SA tries to enhance the exploitation property of the bBOA. The proposed method is tested on twenty datasets from the UCI repository and the results are compared with five popular algorithms for feature selection. The results confirm the effectiveness of the hybrid approach in improving the classification accuracy and selecting the optimal feature subset.

We propose a heuristic approach for solving binary Multiple Instance Learning (MIL) problems, whose objective is to categorize bags of instances. Considering the case with two classes of instances, on the basis of the standard MIL assumption, a bag is classified positive if it contains at least a positive instance and negative if all its instances are negative. Inspired by a well-established MIL Support Vector Machine type approach, our technique is based on iteratively separating the bags by means of successive maximum-margin spheres. Such spheres, whose number is automatically determined, are generated by computing, for each of them, the optimal radius in correspondence to a prefixed center. Numerical results are presented on a set of benchmark test problems, showing the effectiveness of our approach.

The design of binary sequences with low autocorrelation sidelobes is critically important to diverse applications including, radar and sonar as well as communications. Many computational methods have been proposed recently to design binary sequences through optimizing the integrated sidelobe level (ISL, which is a commonly used optimization metric. However, due to the high computational complexities, few of them can be used for long binary sequence designs, which are needed in, for example, low-cost commercial automotive radar systems. To improve the performance of the recently introduced FFT-based CANARY algorithm for long aperiodic binary sequence designs, we introduce a new penalty function into the ISL metric and also propose a new framework of double-loop iterations. These new ideas are also extended to design long periodic binary sequences with low autocorrelation sidelobes and long aperiodic and periodic binary sequences with low correlation zones (LCZs . The associated cost functions are still optimized efficiently via FFT-based iterations, making the proposed algorithms computationally efficient and hence suitable for long binary sequence designs. Moreover, the convergence properties and the computational complexities of these algorithms are analyzed. Numerical examples are provided to demonstrate the effectiveness of the proposed algorithms.

Decades of research suggest that information exchange in groups and organizations can reliably improve judgment accuracy in tasks such as financial forecasting, market research, and medical decision making. However, we show that improving the accuracy of numeric estimates does not necessarily improve the accuracy of decisions. For binary-choice judgments, also known as classification tasks—for example, yes/no or build/buy decisions—social influence is most likely to grow the majority vote share, regardless of the accuracy of that opinion. As a result, initially, inaccurate groups become increasingly inaccurate after information exchange, even as they signal stronger support. We term this dynamic the “crowd classification problem.” Using both a novel data set and a reanalysis of three previous data sets, we study this process in two types of information exchange: (1) when people share votes only, and (2) when people form and exchange numeric estimates prior to voting. Surprisingly, when people exchange numeric estimates prior to voting, the binary-choice vote can become less accurate, even as the average numeric estimate becomes more accurate. Our findings recommend against voting as a form of decision making when groups are optimizing for accuracy. For those cases where voting is required, we discuss strategies for managing communication to avoid the crowd classification problem. We close with a discussion of how our results contribute to a broader contingency theory of collective intelligence. This paper was accepted by Lamar Pierce, organizations.

Fuzzing is a technique of finding bugs by executing a target program recurrently with a large number of abnormal inputs. Most of the coverage-based fuzzers consider all parts of a program equally and pay too much attention to how to improve the code coverage. It is inefficient as the vulnerable code only takes a tiny fraction of the entire code. In this article, we design and implement an evolutionary fuzzing framework called V-Fuzz, which aims to find bugs efficiently and quickly in limited time for binary programs. V-Fuzz consists of two main components: 1) a vulnerability prediction model and 2) a vulnerability-oriented evolutionary fuzzer. Given a binary program to V-Fuzz, the vulnerability prediction model will give a prior estimation on which parts of a program are more likely to be vulnerable. Then, the fuzzer leverages an evolutionary algorithm to generate inputs which are more likely to arrive at the vulnerable locations, guided by the vulnerability prediction result. The experimental results demonstrate that V-Fuzz can find bugs efficiently with the assistance of vulnerability prediction. Moreover, V-Fuzz has discovered ten common vulnerabilities and exposures (CVEs), and three of them are newly discovered.

The US Department of Energy’s fastest supercomputers and forthcoming exascale systems employ Graphics Processing Units (GPUs) to increase the computational performance of compute nodes. However, the complexity of GPU architectures makes tailoring sophisticated applications to achieve high performance on GPU-accelerated systems a major challenge. At best, prior performance tools for GPU code only provide coarse-grained tuning advice at the kernel level. In this article, we describe GPA, a performance advisor that suggests potential code optimizations at a hierarchy of levels, including individual lines, loops, and functions. To gather the fine-grained measurements needed to produce such insights, GPA uses instruction sampling and binary instrumentation to monitor execution of GPU code. At the time of this writing, GPU instruction sampling is only available on NVIDIA GPUs. To understand performance losses, GPA uses data flow analysis to approximately attribute measured instruction stalls back to their causes. GPA then analyzes patterns of stalls using information about a program’s structure and the GPU architecture to identify optimization strategies that address inefficiencies observed. GPA then employs detailed performance models to estimate the potential speedup that each optimization might provide. Experiments with benchmarks and applications show that GPA provides useful advice for tuning GPU code. We applied GPA to analyze and tune a collection of codes on NVIDIA V100 and A100 GPUs. GPA suggested optimizations that it estimates will accelerate performance across the set of codes by a geometric mean of 1.21×. Applying these optimizations suggested by GPA accelerated these codes by a geometric mean of 1.19×.

With the increasing demand for surveillance applications, pedestrian detection has been a topic of interest for many researchers in recent time. The quality of a pedestrian detector is decided in terms of detection accuracy and rate of detection. This paper presents new pedestrian detectors based on two types of classifiers, linear support vector machine and cascade of boosted classifier. These classifiers are trained by using a feature set comprising of the histogram of oriented gradients and dense local difference binary features. Both the image pyramid and non-linear scale space are used to detect pedestrians of various sizes. In order to combine the benefits of the two classifiers, a new two-stage detection scheme is also presented. The detection accuracies of the proposed detectors are studied in terms of miss-rate versus false positive per image and miss-rate versus false positive per window. The performances of the detectors are also compared with the performances of existing detectors of similar type.

Bernoulli multi-armed bandits are a reinforcement learning model used to optimize sequences of decisions with binary outcomes. Well-known bandit algorithms, including the optimal policy, assume that before a decision is made the outcomes of previous decisions are known. This assumption is often not satisfied in real-life scenarios. As demonstrated in this paper, if decision outcomes are affected by delays, the performance of existing algorithms can be severely affected. We present the first practically applicable method to compute statistically optimal decisions in the presence of outcome delays. Our method has a predictive component abstracted out into a meta-algorithm, PARDI, which significantly reduces the impact of delays on commonly used algorithms. We demonstrate empirically that PARDI-enhanced Whittle Index is nearly optimal for a wide range of Bernoulli bandit parameters and delays. In a wide spectrum of experiments it performed better than any other suboptimal algorithm, e.g., UCB1-Tuned and Thompson sampling. PARDI-enhanced Whittle Index can be used when computational requirements of the optimal policy are too high.

In this paper, we propose a Structure-adaptive Neighborhood Preserving Hashing (SNPH) method for unsupervised scalable video search. Unlike most existing hashing methods which equally encode an entire video into a binary feature vector, we propose a neighborhood attention mechanism which encodes the neighborhood-relevant content of a video to better preserve the neighborhood relationships among videos. Motivated by the fact that a video usually contains multiple shots and each shot depicts a different activity, we further develop a structure-adaptive encoder to model the hierarchical structure of the video. Specifically, the encoder adaptively divides each video into multiple segments via detecting temporal boundaries across frames and encodes these segments as a compact binary vector to capture rich structural information. We integrate the neighborhood attention mechanism into the structure-adaptive encoder to learn hash functions that jointly preserve the neighborhood relationships among videos and exploit the hierarchical structure in a video. Experimental results on three widely used benchmark datasets show that our proposed method consistently outperforms state-of-the-art unsupervised video hashing methods.

The class imbalance problem has posed a leading challenge in real-world applications. Traditional methods focus on either the data level or algorithm level to solve the binary classification problem on imbalanced data, and seldom consider searching an effective transformation for classification. Besides, the undersampling process adopted in them is always subjective and unilateral. To address the above issues, we first propose a hybrid classifier ensemble (HCE) framework to conduct binary imbalanced data classification, which mainly includes a metric-based data space transformation (MDST) and an adaptive two-stage undersampling process (ATUP). The MDST aims to find a more appropriate embedding space for original imbalance data sets, and the ATUP considers both informative and representative samples to generate balanced data sets. Furthermore, we design a progressive HCE (PHCE) framework to improve the performance of HCE by utilizing a progressive mechanism with local and global evaluation criteria to select ensemble members. Extensive comparative experiments conducted on 28 real-world data sets exhibit that our method PHCE outperforms the majority of imbalance ensemble classification approaches.

© 2021 Thomas Telford Ltd.Binary granular soil mixtures, as common heterogeneous soils, are ubiquitous in nature and man-made deposits. Fines content and particle size ratio are two important gradation parameters for a binary mixture, which have potential influences on mechanical behaviours. However, experimental studies on drained shear behaviour considering the whole range of fines content and different particle size ratios are scarce in the literature. For this purpose, a series of drained triaxial compression tests was performed on dense binary silica sand mixtures with four different particle size ratios to investigate systematically the effects of fines content and particle size ratio on the drained shear behaviours. Based on these tests, the strength-dilation behaviour and critical state behaviour were examined. It was observed that both fines content and particle size ratio have significant influence on the stress-strain response, the critical state void ratio, the critical state friction angle, the maximum dilation angle, the peak friction angle and the stress-dilatancy relation. The underlying mechanism for the effects of fines content and particle size ratio was discussed from the perspective of the kinematic movements at particle level.

Face recognition is one the most interesting topic in the field in computer vision and image processing. Face recognition is a processing system that recognizes and identifies individuals human by their faces. Automatic face recognition is powerful way to provide, authorized access to control their system. Face recognition has many challenging problems (like face pose, face expression variation, illumination variation, face orientation and noise) in the field of image analysis and computer vision. This method is work on feature extraction part of face recognition. New way to extract face feature using LD-BGP code operator it is like LGS and LBP feature extraction operator. In our LD-BGP-code operator work in two direction first linear then diagonal. In both direction, its create eight digits code to every pixel of image. Means of these two directional are taken so that is cover all neighbor of center pixel. First linear direction, only horizontal and vertical pixel are taken. Second diagonal direction only diagonal pixels taken. In matching phase, we use Euclidean distance to match a face image. We perform the Linear and diagonal directional operator method on face database ORL. We get accuracy 95.3 %. LD-BGP method also works on different type image like illuminated and expression variation image.

Change point detection plays a fundamental role in many real-world applications, where the goal is to analyze and monitor the behaviour of a data stream. In this paper, we study change detection in binary streams. To this end, we use a likelihood ratio between two models as a measure for indicating change. The first model is a single bernoulli variable while the second model divides the stored data in two segments, and models each segment with its own bernoulli variable. Finding the optimal split can be done in \( \mathcal {O} \mathopen {}\left( n\right) \) time, where n is the number of entries since the last change point. This is too expensive for large n. To combat this we propose an approximation scheme that yields \((1 - \epsilon )\) approximation in \( \mathcal {O} \mathopen {}\left( \epsilon ^{-1} \log ^2 n\right) \) time. The speed-up consists of several steps: First we reduce the number of possible candidates by adopting a known result from segmentation problems. We then show that for fixed bernoulli parameters we can find the optimal change point in logarithmic time. Finally, we show how to construct a candidate list of size \( \mathcal {O} \mathopen {}\left( \epsilon ^{-1} \log n\right) \) for model parameters. We demonstrate empirically the approximation quality and the running time of our algorithm, showing that we can gain a significant speed-up with a minimal average loss in optimality.

Digital images used in business, government and medical sectors insist the need for image security against various threats. An exclusive system that can make use of color spaces has been proposed to process sensitive images based on the binary curve. The proposed Encryption systeM fOr proTecting imagEs has been coined as EMOTE. This multilayered system works on binary curves as binary operations have no carries. Since binary squaring is lightweight, it is claimed to be smaller and faster in hardware than prime-field ones. Logistic mapping has been done to perform chaotic masking, which is then followed by DNA encoding in the next layer. ECC over GF(2m) that works on the spatial domain has been chosen as it is one of the proven technique for its mathematical strength. This proposed system can facilitate both symmetric and asymmetric combination and hence hybrid. Experimental results and the histogram analysis obtained comprehend this proposed system for its extensive deployment to process sensitive images, especially in the medical domain. Ideal measures attained for MSE, PSNR values for the standard benchmark images taken from “The whole brain atlas” database from Harvard University, substantiate the proposed EMOTE system as an expedient choice for processing sensitive images.

We present a progressive approximation algorithm for the exact solution of several classes of interdiction games in which two noncooperative players (namely an attacker and a follower) interact sequentially. The follower must solve an optimization problem that has been previously perturbed by means of a series of attacking actions led by the attacker. These attacking actions aim at augmenting the cost of the decision variables of the follower’s optimization problem. The objective, from the attacker’s viewpoint, is that of choosing an attacking strategy that reduces as much as possible the quality of the optimal solution attainable by the follower. The progressive approximation mechanism consists of the iterative solution of an interdiction problem in which the attacker actions are restricted to a subset of the whole solution space and a pricing subproblem invoked with the objective of proving the optimality of the attacking strategy. This scheme is especially useful when the optimal solutions to the follower’s subproblem intersect with the decision space of the attacker only in a small number of decision variables. In such cases, the progressive approximation method can solve interdiction games otherwise intractable for classical methods. We illustrate the efficiency of our approach on the shortest path, 0-1 knapsack and facility location interdiction games. Summary of Contribution: In this article, we present a progressive approximation algorithm for the exact solution of several classes of interdiction games in which two noncooperative players (namely an attacker and a follower) interact sequentially. We exploit the discrete nature of this interdiction game to design an effective algorithmic framework that improves the performance of general-purpose solvers. Our algorithm combines elements from mathematical programming and computer science, including a metaheuristic algorithm, a binary search procedure, a cutting-planes algorithm, and supervalid inequalities. Although we illustrate our results on three specific problems (shortest path, 0-1 knapsack, and facility location), our algorithmic framework can be extended to a broader class of interdiction problems.

Natural disasters such as major storms or hurricanes can seriously threaten the resilience of large and coupled infrastructures such as power distribution and transportation systems. This paper proposes a planning (i.e., investment + operation) method for presenting the enhanced resilience of coupled transportation and power distribution systems. The proposed investment method includes capacity expansion of power lines, roads, and charging stations, and the hardening of roads and power lines. A tri-level problem is proposed and formulated to accommodate random natural disasters. The proposed model is solved by applying Benders decomposition and column-and-constraint (C&CG) algorithms. Benders decomposition will decompose the tri-level coupled problem into a single-level master problem and bi-level subproblem. However, the latter with binary variables is not a convex problem and cannot be converted to the max problem. The C&CG algorithm is applied to solve the bi-level subproblem with binary variables. The proposed resilience enhancement algorithm is tested and numerical results are provided for coupled transportation and power distribution systems with 21 electric buses and 20 roads, to validate the effectiveness of the proposed planning method.

In many practical applications, the data are class imbalanced. Accordingly, it is very meaningful and valuable to investigate the classification of imbalanced data. In the framework of binary imbalanced data classification, the synthetic minority oversampling technique (SMOTE) is the best-known oversampling method. However, for each positive sample, SMOTE generates only k synthetic samples on the lines between the positive sample and its k -nearest neighbors, resulting in three drawbacks: (1) SMOTE cannot effectively extend the training field of positive samples; (2) the generated positive samples lack diversity; (3) SMOTE does not accurately approximate the probability distribution of the positive samples. Therefore, two binary imbalanced data classification methods named BIDC1 and BIDC2 based on diversity oversampling by generative models are proposed. The BIDC1 and BIDC2 conduct diversity oversampling using extreme learning machine autoencoder and generative adversarial network, respectively. Extensive experiments on 26 data sets are conducted to compare the two methods with 14 state-of-the-art methods using five metrics: F-measure, G-means, AUC-area, MMD-score, and Silhouette-score. The experimental results demonstrate that the two methods outperform the other 14 methods.

Reduction of redundancy and improvement of error-correcting capability are essential research themes in the coding theory. The best codes constructed in various ways are recorded in a database maintained by Markus Grassl. In this paper, we propose an algorithm to construct the best code using punctured codes and a supporting method for constructing the best codes. First, we define a new evaluation function to determine deletion bits and propose an algorithm for constructing punctured linear codes. 36 best codes were constructed in the proposed algorithm, and 131 best codes were constructed by further modifying those best codes. Secondly, we evaluate the possibility of increasing the minimum distance based on the relationship between code length, information length, and minimum distance. We narrowed down the target (n,k) code to try the best code search based on the evaluation and found 30 other best codes.

Slip status of drill string is system generated binary value computed by comparison of sensor generated real time hook load value with a minimum threshold value of hook load stored in measurement system. This research article describes a novel method of slip status detection by machine vision technology which helps overcome the constraints of slip status detection with legacy measurement method. It also helps improve the real time drilling data quality and optimize and automate drilling operations. A method to detect drill string slip status with high-resolution digital camera installed on mast near rig floor is described along with backend vision processing and communication modules, which generate binary values of slip status. The binary values are transferred in real time to drilling measurement system of rig to compute other drilling parameters like bit depth, hole depth and stand counters. This method includes deploying active optical sensors at the rig floor, obtaining 1-D, 2-D, or 3D image data, and processing it to obtain the status of drill string. Reliable measurement of slip status by machine vision helps reduce non-productive time (NPT) by reliable real time surveillance of drilling operations.

Since last few years, microarray technology has got tremendous application in many bio-medical researches. However, in order to efficiently recognize and apply this technology into the bio-medical areas is still very difficult and expensive. There are many metaheuristic approaches has been developed with different biological interpretation. Despite the existence of several approaches, there is always a requirement of development of more robust and efficient approach. In this work a new metaheuristic approach is proposed implementing binary shuffled frog leaping algorithm (BSFLA) for gene selection. To obtain an optimal gene subset, 20 different combination of gene subset is extracted from the original dataset. Out of which the optimal gene subset is identified implementing KNN classifier. Superiority of these gene set is shown using few other classifiers such as ANN and SVM. The model performance is also compared with few other metaheuristic approaches such as particle swarm optimization, differential evolution and genetic algorithm.

The appropriate hosts of emitting layers (EMLs) play an important role in determining the overall performance of solution-processed phosphorescent organic light emitting diodes (PhOLEDs). We have investigated the effect of three species of host molecules, 1,3-bis(carbazol-9-yl)benzene (mCP), 10-(4-(5,5dimethylbenzofuro[3,2-c]acridin-13(5H)-yl)phenyl)-10-phenylanthracen-9(10H)-one (DpAn-5BzAc) and poly(9-vinylcarbazole) (PVK), on the performance of solution-processed blue and yellow PhOLEDs. We have found that compared to the widely used single-host EMLs, the devices using the binary blend of mCP: DpAn5BzAc as hosts, can achieve more efficient optoelectrical characteristics. The maximum current efficiencies of 11.84 and 16.61 have been realized for blue and yellow OLEDs, respectively. The superior electroluminescence performance for binary blend host-based PhOLEDs was attributed to the enhanced charge carrier balance and multi-component miscibility, which has a dramatic influence on the morphology of the emissive layer. These results demonstrate the great potential of the multi-hosts in solution-processed organic optoelectronic devices. The development of complementary colour OLEDs with blue and yellow can provide a simple approach to fabricate solution-processed white PhOLEDs.

In 2019, Kavut and Tutdere proved that the covering radii of a class of primitive binary cyclic codes with minimum distance greater than or equal to $r+2$ is $r$, where $r$ is an odd integer, under some assumptions. We here show that the covering radii $R$ of a class of primitive binary cyclic codes with minimum distance strictly greater than $\ell$ satisfy $r\leq R \leq \ell$, where $\ell,r$ are some integers, with $\ell$ being odd, depending on the given code. This new class of cyclic codes covers that of Kavut and Tutdere.

As a special subclass of cyclic codes, BCH codes have wide applications in communication and storage systems. A BCH code of length n over Fq is always relative to an n-th primitive root of unity β in an extension field of Fq, and is called a dually-BCH code if its dual is also a BCH code relative to the same β. The question as to whether a BCH code is a dually-BCH code is in general very hard to answer. In this paper, an answer to this question for primitive narrow-sense BCH codes and projective narrow-sense ternary BCH codes is given. Sufficient and necessary conditions in terms of the designed distances δ will be presented to ensure that these BCH codes are dually-BCH codes. In addition, the parameters of the primitive narrow-sense BCH codes and their dual codes are investigated. Some lower bounds on minimum distances of the dual codes of primitive and projective narrow-sense BCH codes are developed. Especially for binary primitive narrow-sense BCH codes, the new bounds on the minimum distances of the dual codes improve the classical Sidel’nikov bound, and are also better than the Carlitz and Uchiyama bound for large designed distances δ. The question as to what subclasses of cyclic codes are BCH codes is also answered to some extent. As a byproduct, the parameters of some subclasses of cyclic codes are also investigated.

Feature selection is a significant task in the workflow of predictive modeling for data analysis. Recent advanced feature selection methods are using the power of optimization algorithms for choosing a subset of relevant features to get better classification results. Most of the optimization algorithms like genetic algorithm use many controlling parameters which need to be tuned for better performance. Tuning these parameter values is a challenging task for the feature selection process. In this paper, we have developed a new wrapper-based feature selection method called binary teaching learning based optimization (FS-BTLBO) algorithm which needs only common controlling parameters like population size, and a number of generations to obtain a subset of optimal features from the dataset. We have used different classifiers as an objective function to compute the fitness of individuals for evaluating the efficiency of the proposed system. The results have proven that FS-BTLBO produces higher accuracy with a minimal number of features on Wisconsin diagnosis breast cancer (WDBC) data set to classify malignant and benign tumors.

The Binary Iterative Hard Thresholding (BIHT) algorithm is a popular reconstruction method for one-bit compressed sensing due to its simplicity and fast empirical convergence. There have been several works about BIHT but a theoretical understanding of the corresponding approximation error and convergence rate still remains open. This paper shows that the normalized version of BIHT (NBHIT) achieves an approximation error rate optimal up to logarithmic factors. More precisely, using $m$ one-bit measurements of an $s$-sparse vector $x$, we prove that the approximation error of NBIHT is of order $O \left(1 \over m \right)$ up to logarithmic factors, which matches the information-theoretic lower bound $\Omega \left(1 \over m \right)$ proved by Jacques, Laska, Boufounos, and Baraniuk in 2013. To our knowledge, this is the first theoretical analysis of a BIHT-type algorithm that explains the optimal rate of error decay empirically observed in the literature. This also makes NBIHT the first provable computationally-efficient one-bit compressed sensing algorithm that breaks the inverse square root error decay rate $O \left(1 \over m^{1/2} \right)$., Comment: Submitted to a journal

We consider a complex Gaussian wiretap channel with finite-resolution analog-to-digital converters (ADCs) at both the legitimate receiver and the eavesdropper. For this channel, we show that a positive secrecy rate is always achievable as long as the channel gains at the legitimate receiver and at the eavesdropper are different, regardless of the quantization levels of the ADCs. For the achievability, we first consider the case of one-bit ADCs at the legitimate receiver and apply a binary input distribution where the two input points have the same phase when the channel gain at the legitimate receiver is less than that at the eavesdropper, and otherwise the opposite phase. Then the result is generalized for the case of arbitrary finite-resolution ADCs at the legitimate receiver by translating the input distribution appropriately. For the special case of the real Gaussian wiretap channel with one-bit ADCs at both the legitimate receiver and the eavesdropper, we show that our choice of input distribution satisfies a necessary condition of optimal distributions for Wyner codes., This paper was submitted to IEEE Transactions on Information Theory, and was presented in part at ITW 2019 in Visby, Sweden in Aug 2019

The grasshopper optimization algorithm is one of the recently population-based optimization techniques inspired by the behaviours of grasshoppers in nature. It is an efficient optimization algorithm and since demonstrates excellent performance in solving continuous problems, but cannot resolve directly binary optimization problems. Many optimization problems have been modelled as binary problems since their decision variables varied in binary space such as feature selection in data classification. The main goal of feature selection is to find a small size subset of feature from a sizeable original set of features that optimize the classification accuracy. In this paper, a new binary variant of the grasshopper optimization algorithm is proposed and used for the feature subset selection problem. This proposed new binary grasshopper optimization algorithm is tested and compared to five well-known swarm-based algorithms used in feature selection problem. All these algorithms are implemented and experimented assessed on twenty data sets with various sizes. The results demonstrated that the proposed approach could outperform the other tested methods.

Manually picking regularly and densely distributed first breaks (FBs) are critical for shallow velocity-model building in seismic data processing. However, it is time consuming. We employ the fully-convolutional SegNet to address this issue and present a fast automatic seismic waveform classification method to pick densely-sampled FBs directly from common-shot gathers with sparsely distributed traces. Through feeding a large number of representative shot gathers with missing traces and the corresponding binary labels segmented by manually interpreted fully-sampled FBs, we can obtain a well-trained SegNet model. When any unseen gather including the one with irregular trace spacing is inputted, the SegNet can output the probability distribution of different categories for waveform classification. Then FBs can be picked by locating the boundaries between one class on post-FBs data and the other on pre-FBs background. Two land datasets with each over 2000 shots are adopted to illustrate that one well-trained 25-layer SegNet can favorably classify waveform and further pick fully-sampled FBs verified by the manually-derived ones, even when the proportion of randomly missing traces reaches 50%, 21 traces are missing consecutively, or traces are missing regularly.

The separation of azeotropic mixtures, particularly, pressure sensitive azeotropes is intriguing, challenging, and inevitable in chemical industries in comparison to pressure insensitive azeotropic mixtures. The main bottleneck for separating pressure sensitive azeotropes lies in the pressure selection and sequencing of distillation columns. A conundrum in pressure swing distillation for both minimum and maximum boiling azeotropes primarily about the feasibility of column configurations has been discussed in this work. The two column configurations, Low pressure Column-High Pressure Column (LPC-HPC) and High Pressure Column-Low Pressure Column (HPC-LPC) are found to be dependent on the feed composition, type of azeotrope (whether, minimum or maximum), and the effect of pressure on azeotropic composition. This article has explored concomitantly pressure swing distillation technique (continuous one) and the effect of various feed compositions on the column sequencing and process feasibility. A comprehensive strategy for both minimum and maximum boiling azeotropes has been proposed depending upon the effect of pressure on azeotropic composition. Two example case studies have been also presented in support of the proposed strategies for pressure swing distillation technique.

Occupancy grids encode for hot spots on a map that is represented by a two dimensional grid of disjoint cells. The problem is to recursively update the probability that each cell in the grid is occupied, based on a sequence of sensor measurements from a moving platform. In this paper, we provide a new Bayesian framework for generating these probabilities that does not assume statistical independence between the occupancy state of grid cells. This approach is made analytically tractable through the use of binary asymmetric channel models that capture the errors associated with observing the occupancy state of a grid cell. Binary-valued measurement vectors are the thresholded output of a sensor in a radar, sonar, or other sensory system. We compare the performance of the proposed framework to that of the classical formulation for occupancy grids. The results show that the proposed framework identifies occupancy grids with lower false alarm and miss detection rates, and requires fewer observations of the surrounding area, to generate an accurate estimate of occupancy probabilities when compared to conventional formulations.

Diameter perfect codes form a natural generalization for perfect codes. They are based on the code-anticode bound which generalizes the sphere-packing bound. The code-anticode bound was proved by Delsarte for distance-regular graphs and it holds for some other metrics too. In this paper we prove the bound for non-binary constant-weight codes with the Hamming metric and characterize the diameter perfect codes and the maximum size anticodes for these codes. We distinguish between six families of non-binary diameter constant-weight codes and four families of maximum size non-binary constant-weight anticodes. Each one of these families of diameter perfect codes raises some different questions. We consider some of these questions and leave lot of ground for further research. Finally, as a consequence, some t-intersecting families related to the well-known Erd\"{o}s-Ko-Rado theorem, are constructed.