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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.

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.

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.

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.

Homogeneity, which plays an essential role in decision making, economics and image processing, reflects the regularity of aggregation functions with respect to the inputs with the same ratio. Quasi-homogeneity is a relaxed homogeneity that reflects the original output as well as the same ratio. This paper is devoted to the characterization of all homogeneous/quasi-homogeneous binary aggregation functions in terms of single-argument functions.

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.

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.

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.

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

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.

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.

This paper formulates a fuzzy logic neuron that uses n-uninorms to construct uni-nullneurons. A fuzzy neural network (FNN) composed of these neurons is easy to operate with nullnorms and uninorms at different times, which results in high accuracy of the model outputs and increases the flexibility in connecting the rule antecedents (enabling AND and OR connections within one rule). This, in turn, may allow experts/operators to extract more knowledge from data. The FNN uses a one-versus-rest classifier learning scheme for multi-class classification problems, where neuron activation levels construct the (indicator) regression matrix; this results in a non-linear regression by indicator, which can resolve the inherent class masking problem apparent in the linear case. We propose a specific neuron-selection strategy in the learning stage that applies Lasso to bootstrap replications in order to ensure that the rule base is as compact as possible and induced by a low number of neurons. To evaluate the new neuron acting in FNNs, we performed pattern classification, and regression tests. Compared with traditional FNN in the literature, our variant showed improved model accuracies for several high-dimensional real-world datasets in binary and multi-class classification and regression problems. Combined with the ability to generate human-readable rules, this offers the ability to generate parsimonious responses with a high degree of confidence.

Facing the challenge of small open labeled data sets in remote sensing change detection, this letter proposes a novel supervised change detection method by taking advantages of capsule network which can reach the same performance as traditional convolutional neural networks (CNNs) but with less training data. To achieve this aim, we propose a pseudo-Siamese capsule network which takes both rotational invariance and spatial hierarchies between features into account for aerial images change detection. First, the features of image pairs are extracted by two identical nonshared weights convolutional capsule networks. Second, the extracted features are directly concatenated and sent to another convolutional capsule layer. The change probability map is obtained by calculating the length of the capsule vectors in the final layer. Additionally, to reduce the influence of imbalance samples when we optimize our network, we design a margin-focal loss function to pay more attention to the misclassified samples. Finally, binary change map can be produced by a simple threshold. Experimental results carried out on the SZTAKI AirChange Benchmark Set show that the proposed method achieves comparable and even better results with existing state-of-the-art methods in terms of F-measure.

The study of refractive index of liquids becoming increasingly popular in diverse areas to monitor chemical, pharmaceutical, food, scientific, biochemical, medical, etc. processes and procedures. In the present paper a low cost, simple, compact, reliable, robust, miniaturized and sturdy Refractive Index sensor is reported. It is configured by using a set of two multimode PCS fiber of 50 cm length in each, connected one to the source of wavelength 660 nm and other to a light detector. A glass rod drawn in the form of ‘U’, of compatible dimensions to that of two multimode fibers, was connected between source and detector by jointing to the remaining ends of the fibers, thus creating a sensing zone. Maintaining the binary liquids of Toluene and t-Butanol at the sensing zone at various temperatures, the light launched from source was monitored in the detector at the detector end. By forming relationship between index of refraction and power output at the temperature range of 10 °C to 60 °C, the sensor expected to be used to detect the index of refraction of liquids with maximum resolution of the order of 10-5 in the operating range from 1.36312nD to 1.50915nD.

This paper presents the study of binary component analysis subjected to distillation process using Aspen Plus. Binary components namely Isopropyle alcohol and water were considered for the study and their characteristics are analyzed at certain temperature and pressure. Aspen Plus provides virtual exposure of physical process, gives quantitative measure of mole fraction of liquid phase, vapor phase with changes in temperature, pressure and activity coefficients. Steady state and dynamic simulation results depicts the behavior of the system and enables the user to understand how the system reacts in virtual environment, to realize the system behavior in real-time environment.

This paper presents a simple, yet effective method for dynamic beamforming that can be readily realized by integrating a tunable binary Huygens' metasurface with a leaky-waveguide antenna. Heretofore, dynamic beamforming has been difficult to achieve owing to the challenges in attaining full 360 of dynamic phase tunability, while also independently controlling the local aperture amplitudes from zero to unity. In contrast, the hereby proposed method only requires 90 of dynamic phase tunability (with arbitrary transmission amplitudes), thereby significantly alleviating the stringent tuning requirements. In particular, the proposed method is obtained by leveraging the antenna-array and holography theories, and utilizing the idea of ‘virtual’ electric line sources. Based on full-wave simulations, we demonstrate the versatility of the proposed method through the designs of a Huygens'-metasurface-assisted leaky-waveguide antenna that generates various complex far-field patterns such as sector beams and Dolph-Chebyshev patterns.

In this paper, error performance of a communication link subjected to a non-Gaussian noise model known as symmetric alpha-stable (S α S) noise and α − μ fading environment is analyzed with selection combining at the receiver. To this end, using binary phase-shift keying (BPSK), closed-form expressions of exact and asymptotic bit-error-rate (BER) are obtained. Furthermore, closed-form expressions of outage probability and n -th moment are obtained. From the n -th moment, the average signal-to-noise ratio (SNR) and channel quality estimation index (CQEI) are calculated. The effect of the number of antennas (L) and the fading parameters on the BER is studied for different impulsive settings. Monte-Carlo simulations are performed to validate the derived results.

The extension of the translated–consistent Peng–Robinson (tc-PR) equation of state (EoS) to mixtures has been investigated. For this purpose, advanced EoS/aresE,γ mixing rules are used to combine t...

A new class of graphs G(w), called Parikh word representable graphs (PWRG), corresponding to words $w$ that are finite sequence of symbols, was considered in the recent past. Several properties of these graphs have been established. In this paper, we consider these graphs corresponding to binary core words of the form $aub$ over a binary alphabet {a,b}. We derive formulas for computing the Wiener index of the PWRG of a binary core word. Sharp bounds are established on the value of this index in terms of different parameters related to binary words over {a,b} and the corresponding PWRGs. Certain other Wiener-type indices that are variants of Wiener index are also considered. Formulas for computing these indices in the case of PWRG of a binary core word are obtained.

It is well-known that there are instances of the binary paint shop problem for which the recursive greedy heuristic is better than the greedy heuristic. In this note, we give an example of a family of instances where the greedy heuristic is better than the recursive greedy heuristic, thus showing that these heuristics are uncorrelated.

Majority voting is often employed as a tool to increase the robustness of data-driven decisions and control policies, a fact which calls for rigorous, quantitative evaluations of the limits and the potentials of majority voting schemes. This letter focuses on the case where the voting agents are binary classifiers and introduces novel bounds on the probability of misclassification conditioned on the size of the majority. We show that these bounds can be much smaller than the traditional upper bounds on the probability of misclassification. These bounds can be used in a 'Probably Approximately Correct' (PAC) setting, which allows for a practical implementation.

The exponential increase in the rate of data size is much higher than the increase in the speed of the computer, which has given much focus to search algorithms in the research literature. Finding an item in an ordered dataset is an efficient method in the data processing. However, binary and interpolation algorithms are commonly used to search ordered datasets in many applications. In this paper, we propose a hybrid algorithm for searching ordered datasets based on the idea of interpolation and binary search. The proposed algorithm is called Interpolated Binary Search (IBS). It is well known that the performance of traditional interpolation search depends specifically on key distribution, and its performance degrades significantly in non-uniform distributed datasets. Therefore, our proposed algorithm works efficiently on various distribution datasets. In particular, IBS aims to search datasets of unknown distribution or datasets that change dynamically and produce a dynamic distribution. Experimental results show that IBS performs better compared to other algorithms that use a similar approach.

Mixing and heat transfer among particles in rotating drums are widely applied in numerous industrial processes. Generally, mixing and heat transfer occur simultaneously. Consequently, the interrelationship between mixing and heat transfer must be investigated for industrial applications. In this study, the radial mixing and heat transfer of spherical granular materials (3 mm) with various properties in batch rotating drums are investigated numerically using the discrete element method. The evolution of mixing and heat transfer characteristics with rotation speed is analyzed from the perspective of time and number of revolutions, respectively. The results indicate that, depending on the physical parameters and thermal properties of particles, the mixing quality does not always accurately reflect the heat transfer effect. In binary granular beds of monodispersed spherical particles with different densities and thermal diffusivities, the main heat transfer mechanism is related to the ratio of the thermal conductivity of the particles to that of the fluid.

Recently, several classes of cutting planes have been introduced for binary polynomial optimization. In this paper, we present the first results connecting the combinatorial structure of these inequalities with their Chvátal rank. We determine the Chvátal rank of all known cutting planes and show that almost all of them have Chvátal rank 1. We observe that these inequalities have an associated hypergraph that is β-acyclic. Our second goal is to derive deeper cutting planes; to do so, we consider hypergraphs that admit β-cycles. We introduce a novel class of valid inequalities arising from odd β-cycles, that generally have Chvátal rank 2. These inequalities allow us to obtain the first characterization of the multilinear polytope for hypergraphs that contain β-cycles. Namely, we show that the multilinear polytope for cycle hypergraphs is given by the standard linearization inequalities, flower inequalities, and odd β-cycle inequalities. We also prove that odd β-cycle inequalities can be separated in linear time when the hypergraph is a cycle hypergraph. This shows that instances represented by cycle hypergraphs can be solved in polynomial time. Last, to test the strength of odd β-cycle inequalities, we perform numerical experiments that imply that they close a significant percentage of the integrality gap.

Emerging memory technologies rely on Multilevel Cells (MLC) to achieve high density; the use of multiple levels per cell allows storage of multiple bits, but it also reduces the margins and makes it error prone. Error control codes (including error correction and detection codes) can be used to protect MLC memories from errors; however, most existing coding schemes have been designed for traditional binary memories (so storing a single bit). In MLC memories, errors cause a change from a level to an adjacent level or to the next one (depending on the employed technology), so they are often referred to as limited magnitude errors. For a binary coding of levels to bits, these limited magnitude errors can corrupt several bits making traditional coding schemes inefficient. In this paper, error detection of MLC memories is considered when a binary encoding of levels to bits is used and two new schemes are proposed: One-Bit Parity (OBP) and Two-Bit Parity (TBP). The first scheme targets errors of magnitude-1 for detection using a single parity bit that checks only one bit per cell. The second scheme detects both magnitude-1 and -2 errors using only two parity bits. Both schemes are compared to existing alternatives, namely Gray coding combined with a single parity bit (GP) for OBP and Interleaved Parity (IP) for TBP. The results show that OBP reduces the encoding and error detection circuitry complexity and delay, while TBP additionally reduces the number of parity bits for some configurations. Therefore, OBP and TBP can be efficient alternatives for detection of limited magnitude errors in MLC memories that use a binary encoding of levels to bits.

In a type-2 fuzzy logic system, one of the important operations is to calculate the centroid of an interval type-2 fuzzy set (IT2 FS). In this paper, two novel algorithms called binary algorithms are proposed to calculate the centroid of IT2 FSs. Then, the outputs of the proposed binary algorithms are proven to be the optimal values. After analyzing the computational complexities of Karnik-Mendel (KM) algorithms, enhanced Karnik-Mendel (EKM) algorithms, enhanced iterative algorithms based on stopping condition (EIASC) algorithms and the proposed binary algorithms, it is found that the proposed binary algorithms are superior to the KM algorithms, EKM algorithms and EIASC algorithms. Finally, two extended binary algorithms are proposed to compute the centroid of an IT2 FS. The efficiencies of the proposed binary algorithms and extended binary algorithms are demonstrated by extensive simulations.

Quantized neural networks (NN) with reduced bit precision are practical solutions to minimize computational and memory resource requirements and play a vital role in machine learning. However, it is still challenging to avoid significant accuracy degradation due to numerical approximation and lower redundancy. In this paper, a novel robustness-aware 2-bit quantization scheme (RAQ) is proposed for NN, based on binary NN and generative adversarial networks (GAN), which improve performance by enriching binary NN information, extracting the structural information and considering the robustness of the quantized NN. Specifically, using a shift-add operation to replace the multiply-accumulate in the quantization process can speed the NN. A structural loss is proposed to represent the difference between the original NN and quantized NN, such that the structural information of data is preserved after quantization. The structural information learned from NN plays an important role in improving the performance and allows for further fine-tuning of the quantized NN by applying the Lipschitz constraint to the structural loss. For the first time, we consider the robustness of the quantized NN and propose a non-sensitive perturbation loss function by introducing an extraneous term of the spectral norm. The experiments were conducted on CIFAR-10, SVHN and ImageNet datasets with popular NN (such as MobileNetV2, ResNet20, etc.). Extensive experiments show that our new 2-bit quantization scheme is more efficient than the state-of-the-art quantization methods. Our scheme effectively reduced the latency by 2 × and the accuracy decline by 1–4%. Meanwhile, the experimental results also demonstrate that the RAQ is robust with adversarial attacks, we not only eliminate the robustness gap between full-precision and quantized models, but also improve the robustness over full-precision ones by 10%.

In Multi-label problems, each instance is associated with a set of predefined labels. Binary Relevance, as a common approach, uses one binary classifier for each label and ignores the probable dependencies between some labels. Stacked-Binary Relevance (SBR) was proposed to consider the label dependencies, by augmenting a second layer of binary models using the predicted labels of the first level binary models as additional features. By reusing all predicted labels, SBR implicitly assumes full dependencies between labels, which is not usually a true assumption in the real world. Moreover, SBR uses a constant threshold in decision functions of the binary models, while adjusting the threshold for each label specially for imbalanced ones can improve the performance. This paper proposes a k-Nearest Neighbor stacking method that adjusts the thresholds in decision functions of the binary classifiers and uses a feature-weighted distance measure to reduce the effect of irrelevant labels in stacking. The method can leverage positive/negative and symmetric/asymmetric label dependencies expressed as feature weights. Also, it can tackle the main shortcomings of SBR (revealed in the existence of irrelevant labels and imbalanced data). Using 22 multi-label datasets, the proposed method is assessed and outperforms state-of-the-art methods presented in the literature.

The binary-sized pebble mixed beds at the maximum packing efficiency state were numerical simulated by the DEM simulation to investigate the effect of fixed wall and pebble size ratios. The evolution of the distribution of packing fraction and porosity, radial distribution function and contact force were given and analyzed with emphasis on the effects of the fixed wall and pebble size ratio. The results showed that the fixed wall will result in a reduction of average packing fraction and an obvious wall effect in local porosity distribution. With the increase of the pebble size ratio, the volume fraction of the wall affected regions gradually decrease. The variation of the local packing fraction of the binary-sized pebble bed is mainly determined by the partial packing fraction of large pebbles and the partial packing fraction of small pebbles, respectively, in the regions close to the fixed wall. Furthermore, the fixed wall has little effect on the radial distribution function and contact force. However, the pebble size ratios have great influence on the radial distribution function and the contact force in binary-sized pebble bed at the maximum packing efficiency state. With the increase of the pebble size ratio, the radial distribution functions of the whole pebble beds are consistent with that of small pebbles, and approach to that of the mono-sized pebble bed. In addition, with the increase of the pebble size ratio, a higher contact force can be obtained in pebble beds.

The ZrO2–TiO2 and ZrO2-YO1.5 binary systems have been reassessed based on the latest literature information, then the thermodynamic parameters of the two systems combine with that of the YO1.5-TiO2 system have been used to extrapolate the thermodynamic database of ZrO2-YO1.5-TiO2 ternary system. Based on the available experimental information, the ternary interaction parameters of liquid, F (fluorite), and P (pyrochlore) phases are introduced to fix this ternary system. Especially, the calculated solid solubility limit of Tss (tetragonal) and F phases are first considered in this work, which are consistent with the reported experiments. Finally, the isothermal sections calculated at 1573 K, 1773 K, 1823K 1873K and 1923K (1300 °C, 1500 °C, 1550 °C, 1600 °C and 1650 °C) are plotted, which are more consistent with the experimental results than other calculations.

Cross-modal hashing is for the purpose of compressing heterogeneous multi-modal data into compact binary codes for the cross-modal retrieval, where accuracy and efficiency are two primary issues. To achieve high accuracy and efficiency, we put forward a novel method named Fast Unmediated Hashing (FUH) for cross-modal retrieval. For this method, motivated by the fact that label vector is a natural binary representation of samples for retrieval, we directly learn the cross-modal hash codes from semantic labels without any intermediate representation. This will capture more relations among different modalities, and reduce the number of variables. However, directly learning hash codes from labels would weaken the discrimination of hash codes. To address this issue, double supervision involving label information and pairwise similarity is proposed to enhance the discrimination. In addition, to decrease the training time, we present a strategy to bypass the similarity matrix-related operation in each iteration of optimization, thus some other related terms can also be computed offline to lower training complexity. Compared to several state-of-the-art techniques on three public datasets, the experimental results have manifested the superiority of FUH concerning efficiency and accuracy.

Purpose The analysis of boundary layers is needed to reflect the behaviour of fluid flows in current industrial processes and to improve the efficacy of products. Hence, this study aims to analyse the flow and heat transfer performance of hybrid alumina-copper/water (Al2O3-Cu/H2O) nanofluid with the inclusion of activation energy and binary chemical reaction effect towards a moving wedge. Design/methodology/approach The multivariable differential equations with partial derivatives are converted into a specific type of ordinary differential equations by using valid similarity transformations. The reduced mathematical model is elucidated in the MATLAB system by using the bvp4c procedure. This solution method is competent in delivering multiple solutions once appropriate assumptions are supplied. Findings The results of multiple control parameters have been studied, and the findings are verified to provide more than one solution. The coefficient of skin friction was discovered to be increased by adding nanoparticles volume fraction from 0% to 0.5% and 1%, by almost 1.6% and 3.2%. Besides, increasing the nanoparticles volume fraction improves heat transfer efficiency gradually. The inclusion of the activation energy factor displays a downward trend in the mass transfer rates, consequently reducing the concentration profile. In contrast, the increment of the binary reaction rate greatly facilitates the augmentation of mass transfer rates. There is a significant enhancement in the heat transfer rate, approximately 13.2%, when the suction effect dominates about 10% in the boundary layer flow. Additionally, the results revealed that as the activation energy rises, the temperature and concentration profiles rise as well. It is proved that the activation energy parameter boosts the concentration of chemical species in the boundary layer. A similar pattern emerges as the wedge angle parameter increases. The current effort aims to improve the thermal analysis process, particularly in real-world applications such as geothermal reservoirs, chemical engineering and food processing, which often encountered mass transfer phenomenon followed by chemical reactions with activation energy. Originality/value The present results are original and new for the study of flow and heat transfer over a permeable moving wedge in a hybrid nanofluid with activation energy and binary chemical reaction.

The moving granular filter technology based on size segregation is a promising technology for hot gas cleanup. A moving granular bed that can separate a mixture of particles spontaneously and replace the particles continuously layer by layer was design in this work. We experimentally studied the effects of free slope length (L), particle replacement rate (Q), and inlet large particle mass fraction (α) on the degree of size segregation. Results showed that the degree of size segregation increased with an increase in L and decreased with an increase in α. Q exerted little effect on size segregation. The control of the layer thickness and flow velocity of the particles after segregation was achieved by changing α and the angle of the movable plate (θ). We also captured the size segregation process of the particles with a camera and analyzed the influence mechanisms of L, Q, and α on size segregation.

Let $1 be the ordered primitive roots modulo ${p}$ . We study the pseudorandomness of the binary sequence $(\text {s}_ {n})$ defined by ${s}_{n}\equiv \text {g}_{n+1}+{g}_{n+2}\bmod 2,\,\, {n}=0,1,\ldots $ In particular, we study the balance, linear complexity and 2-adic complexity of $({s}_{n})$ . We show that for a typical ${p}$ the sequence $({s}_{n})$ is quite unbalanced. However, there are still infinitely many p such that $({s}_{n})$ is very balanced. We also prove similar results for the distribution of longer patterns. Moreover, we give general lower bounds on the linear complexity and 2-adic complexity of $({s}_{n})$ and state sufficient conditions for attaining their maximums. Hence, for carefully chosen ${p}$ , these sequences are attractive candidates for cryptographic applications.

Multi-sensor millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) wireless sensor networks (WSNs) relying on both distributed (D-MIMO) and centralized (C-MIMO) configurations are conceived. Hybrid combining based low complexity fusion rules are constructed for the fusion center (FC) for both D-MIMO and C-MIMO systems employing a partially connected structure (PCS) and a fully connected structure (FCS), respectively. The decision rules are based on the transmission of local binary sensor decisions and also take into account the accuracy of local detection at the individual sensors. Closed-form analytical expressions are derived for the probabilities of false alarm and correct detection to analyze the system’s performance. Furthermore, the asymptotic distributed detection (DD) performance corresponding to both antenna architectures is analyzed in the large-scale antenna regime along with the pertinent power scaling laws. Additionally, digital signaling matrices are designed for enhancing the system performance. Our simulation results quantify the performance gains of the proposed architectures, which closely match the analytical results.

A CMOS image sensor (CIS) that can perform on-chip binary convolution is presented. The CIS can greatly reduce memory usage and computational complexity by directly generating a feature map for a binary neural network. The pixel readout of the CIS is performed in the column-parallel fashion using incremental delta-sigma analog-to-digital converters (ADCs). The CIS operates in two different modes: convolution and normal modes. When the column ADC is working in the convolution mode, it works as a first-order delta-sigma ADC and generates convolved images using a binary kernel. In the normal operation mode, the ADC is switched to a second-order delta-sigma ADC with little hardware modification and used to capture high-quality images. To demonstrate the CIS architecture, a 192 × 128-pixel CIS, which occupies an active die area of 14.44 mm2, is fabricated in a 0.18 μm standard CMOS process. The performance of the CIS is evaluated through measurements and network simulations. In the normal operation mode, the CIS achieves a read noise of 14.79 e-rms and a full-well capacity of 6,420 e- with a resulting dynamic range of 53 dB. The power consumptions of the CIS are 49.2 and 52.5 mW during the normal and convolution modes, respectively.

We present a neural architecture search (NAS) technique to enhance image denoising, inpainting, and super-resolution tasks under the recently proposed Deep Image Prior (DIP). We show that evolutionary search can automatically optimize the encoder-decoder (E-D) structure and meta-parameters of the DIP network, which serves as a content-specific prior to regularize these single image restoration tasks. Our binary representation encodes the design space for an asymmetric E-D network that typically converges to yield a content-specific DIP within 10--20 generations using a population size of 500. The optimized architectures consistently improve upon the visual quality of classical DIP for a diverse range of photographic and artistic content.

The multilinear polytope of a hypergraph is the convex hull of a set of binary points satisfying a collection of multilinear equations. We introduce the running intersection inequalities, a new class of facet-defining inequalities for the multilinear polytope. Accordingly, we define a new polyhedral relaxation of the multilinear polytope, referred to as the running intersection relaxation, and identify conditions under which this relaxation is tight. Namely, we show that for kite-free beta-acyclic hypergraphs, a class that lies between gamma-acyclic and beta-acyclic hypergraphs, the running intersection relaxation coincides with the multilinear polytope and it admits a polynomial size extended formulation.

Binary bent functions have a strictly specified number of non-zero values. In the same way, ternary bent functions satisfy certain requirements on the elements of their value vectors. These requirements can be used to specify six classes of ternary bent functions. Classes are mutually related by encoding of function values. Given a basic ternary bent function, other functions in the same class can be constructed by permutation matrices having a block structure similar to that of the factor matrices appearing in the Good-Thomas decomposition of Cooley-Tukey Fast Fourier transform and related algorithms.

We propose a novel soft-aided iterative decoding algorithm for product codes (PCs). The proposed algorithm, named iterative bounded distance decoding with combined reliability (iBDD-CR), enhances the conventional iterative bounded distance decoding (iBDD) of PCs by exploiting some level of soft information. In particular, iBDD-CR can be seen as a modification of iBDD where the hard decisions of the row and column decoders are made based on a reliability estimate of the BDD outputs. The reliability estimates are derived by analyzing the extrinsic message passing of generalized low-density-parity check (GLDPC) ensembles, which encompass PCs. We perform a density evolution analysis of iBDD-CR for transmission over the additive white Gaussian noise channel for the GLDPC ensemble. We consider both binary transmission and bit-interleaved coded modulation with quadrature amplitude modulation. We show that iBDD-CR achieves performance gains up to 0.51 dB compared to iBDD with the same internal decoder data flow. This makes the algorithm an attractive solution for very high-throughput applications such as fiber-optic communications.

Distributed detection in wireless sensor networks is considered, where the decisions made by local sensors are transmitted towards a fusion center through parallel binary symmetric channels. Our contribution is to show that, when the number of local sensors approaches infinity and the channel capacity from the local sensor to the fusion center is larger than the entropy of any local decision, there exists one method in theory that the local decision can be sent to the fusion center without distortion. Correspondingly, the asymptotically optimal link bit error probability between each sensor and the FC is determined. The resulting distributed detection system can reach asymptotically the same detection performance as that with ideal communication. Although the proposed method is asymptotically optimal in nature, it is valid to the distributed detection system when the quantity of the sensors is above dozens. The simulation confirms its validity. Moreover, when compared with the ideal one, simulations confirmed that the proposed scheme achieves a significant saving of communication resource at the expense of a small reduction in the detection performance for the system with dozens of sensors.

We face a binary multiple instance learning (MIL) problem, whose objective is to discriminate between two kinds of point sets: positive and negative. In the MIL terminology, such sets are called bags, and the points inside each bag are called instances. Considering the case with two classes of instances (positive and negative) and inspired by a well-established instance-space support vector machine (SVM) model, we propose to extend to MIL classification the proximal SVM (PSVM) technique that has revealed very effective for supervised learning, especially in terms of computational time. In particular, our approach is based on a new instance-space model that exploits the benefits coming from both SVM (better accuracy) and PSVM (computational efficiency) paradigms. Starting from the standard MIL assumption, such a model is aimed at generating a hyperplane placed in the middle between two parallel hyperplanes: the first one is a proximal hyperplane that clusters the instances of the positive bags, while the second one constitutes a supporting hyperplane for the instances of the negative bags. Numerical results are presented on a set of MIL test data sets drawn from the literature.

Semiconductor materials for energy storage are the core and foundation of modern information society and play important roles in photovoltaic system, integrated circuit, spacecraft technology, lighting applications, and other fields. Unfortunately, due to the long experiment period and high calculation cost, the high-precision band gap (the basic characteristic parameter) of semiconductor is difficult to obtain, which hinders the development of new semiconductor materials. Since the traditional Perdew–Burke–Ernzerhof (PBE) functional not only requires a long calculation time, but also significantly underestimates the band gap, we developed a deep learning model that can predict the more precise Heyd–Scuseria–Ernzerhof (HSE06) band gaps in milliseconds for 1,503 binary metallic oxides, nitrides, and sulfides, with a mean absolute error (MAE) of 0.35 eV, a mean squared error (MSE) of 0.21 eV, and a coefficient of determination (R2) of 0.98. Based on transfer learning, only 106 crystals), which is of great significance for the screening and discovery of new semiconductor energy storage materials.

In this work, we describe strategies that help process designers in the identification and design of efficient distillation schemes for separating binary mixtures. Over the decades, numerous distillation schemes have been proposed in the literature as more efficient alternatives for the conventional heat-supplied distillation. However, a comprehensive analysis that helps to navigate through this large landscape of distillation schemes remains unavailable at one source. Here, we present numerous insights in the form of observations and remarks that explain why and when the alternatives are more efficient than the conventional heat-supplied distillation. The need for this article becomes apparent given that an understanding has begun to emerge that distillation is inherently inefficient and it needs to be replaced with alternative technologies to make industrial separations more sustainable. The main reason behind such general statements is either the use of an incorrect definition of efficiency or the consideration of an inefficient mode of operation. Here, we elucidate the correct approach to understand the efficiency of distillation, and demonstrate that it can be very efficient when the right operating mode is chosen.

The joint signatures of binary-state and multi-state (semi-coherent or mixed) systems with i.i.d. (independent and identically distributed) binary-state components are considered in this work. For the comparison of pairs of binary-state systems of different sizes, transformation formulas of their joint signatures are derived by using the concept of equivalent systems and a generalized triangle rule for order statistics. Similarly, for facilitating the comparison of pairs of multi-state systems of different sizes, transformation formulas of their multi-state joint signatures are also derived. Some examples are finally presented to illustrate and to verify the theoretical results established here.