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Fully homomorphic encryption (FHE) cryptographic systems enable limitless computations over encrypted data, providing solutions to many of today's data security problems. While effective FHE platforms can address modern data security concerns in unsecure environments, the extended execution time for these platforms hinders their broader application. This project aims to enhance FHE systems through an efficient parallel framework, specifically building upon the existing torus FHE (TFHE) system chillotti2016faster. The TFHE system was chosen for its superior bootstrapping computations and precise results for countless Boolean gate evaluations, such as AND and XOR. Our first approach was to expand upon the gate operations within the current system, shifting towards algebraic circuits, and using graphics processing units (GPUs) to manage cryptographic operations in parallel. Then, we implemented this GPU-parallel FHE framework into a needed genomic data operation, specifically string search. We utilized popular string distance metrics (hamming distance, edit distance, set maximal matches) to ascertain the disparities between multiple genomic sequences in a secure context with all data and operations occurring under encryption. Our experimental data revealed that our GPU implementation vastly outperforms the former method, providing a 20-fold speedup for any 32-bit Boolean operation and a 14.5-fold increase for multiplications.This paper introduces unique enhancements to existing FHE cryptographic systems using GPUs and additional algorithms to quicken fundamental computations. Looking ahead, the presented framework can be further developed to accommodate more complex, real-world applications. [ABSTRACT FROM AUTHOR]

Multimedia security is important as privacy and protection has to be ensured when images from military, medical and other confidential services are sent through the unprotected channel. In this paper, an attempt to develop reconfigurable hardware for an encryption scheme using FPGA has been carried out and tested successfully for its features. In this proposed encryption scheme, chaos and a novel artificial neural network-based diffusion have been used along with zig-zag permutation for pixel level and novel enhanced Fisher-Yates permutation for bit level. The ANN is composed of additive and multiplicative convolution and the 2D cellular automata as an activation function for diffusion. A novel key generation algorithm is adopted to produce the key values for generating the chaos sequence. Every image gets a new key, hence differential attack possibility is negligible. The encryption algorithm is designed in MATLAB and coded in Verilog HDL to develop it as reconfigurable hardware. The hamming distance is analyzed to know the difference between the key sequence generated. The average hamming distance is 0.5111 and the highest value is 0.8796 to show that the randomness of the sequence. Similarly, equidistribution, autocorrelation and periodicity analysis are performed. The NIST SP 800–22 randomness test was also performed on keys and encrypted images to analyze its randomness. The results obtained from the encryption process are tested for their efficiency related to statistical and differential attacks. It gives better results when compared with a few encryption algorithms in the literature. [ABSTRACT FROM AUTHOR]

Analyzing electroencephalographic signals (EEG) could provide valuable information about functional neural activity (FNA) during human motion. The hypothesis of this work is twofold: spatial patterns emerge in EEG signals from functional connectivity (FC) analysis during lower limb movements, and the spatial patterns are mosto robust in some frequency bands than in others. Accordingly, a set of human subjects without neuromotor pathologies participated in an experimental trial where EEG signals were recorded during lower limb movements. The FC was studied with coherence analysis (in δ , θ , and α) and graph theory was proposed to study the characteristics of spatial dynamics by means a set of metrics (degree, maximum connection, and closeness centrality) and two distances (Hamming distance and Jaccard). Finally, a statistical study of the metrics by frequency band was performed to analyze the significant differences between the phases of each stage and movement, considering the proposed metrics. The results of the study indicated that the frequency bands that showed greater statistical significance in the analysis were δ , θ , and α and that the major differences in graph dynamics were shown in degree, maximum connection, and closeness centrality in α band. Present findings portray leading underlying neural networks, implying that discernible spatial patterns exist in FNA during lower limb movements, and such patterns can be characterized with the proposed methodology. [ABSTRACT FROM AUTHOR]

The complement of the highest result of multiplication of two neutrosophic graphs is determined in this study. In the complement of the maximum product of a neutrosophic graph, the degree of a vertex is investigated. The complement of the maximum product of two normal neutrosophic graphs has several results that are presented and proven. Finally, we have offered a neutrosophic graph application for locating an online streaming service using normalized Hamming distance. [ABSTRACT FROM AUTHOR]

In order to safeguard image copyrights, zero-watermarking technology extracts robust features and generates watermarks without altering the original image. Traditional zero-watermarking methods rely on handcrafted feature descriptors to enhance their performance. With the advancement of deep learning, this paper introduces "ZWNet", an end-to-end zero-watermarking scheme that obviates the necessity for specialized knowledge in image features and is exclusively composed of artificial neural networks. The architecture of ZWNet synergistically incorporates ConvNeXt and LK-PAN to augment the extraction of local features while accounting for the global context. A key aspect of ZWNet is its watermark block, as the network head part, which fulfills functions such as feature optimization, identifier output, encryption, and copyright fusion. The training strategy addresses the challenge of simultaneously enhancing robustness and discriminability by producing the same identifier for attacked images and distinct identifiers for different images. Experimental validation of ZWNet's performance has been conducted, demonstrating its robustness with the normalized coefficient of the zero-watermark consistently exceeding 0.97 against rotation, noise, crop, and blur attacks. Regarding discriminability, the Hamming distance of the generated watermarks exceeds 88 for images with the same copyright but different content. Furthermore, the efficiency of watermark generation is affirmed, with an average processing time of 96 ms. These experimental results substantiate the superiority of the proposed scheme over existing zero-watermarking methods. [ABSTRACT FROM AUTHOR]

Handwritten signatures are unique behavioural attributes that serve as a distinct form of identification for individuals. This study focuses on the analysis of signature patterns using various parameters, including aspect ratio, angle of the first letter with respect to the baseline, and the ratio of the area of a circle to its radius within the signature. A dataset consisting of 1200 genuine and 1200 simulated (English) signature samples from 12 individuals were examined using an image analysis tool to record measurements for these parameters. The primary objectives were to investigate the consistency of signatures within the same person and to compare genuine signatures with simulated ones. This study contributes to the field of forensic investigations by offering insights into the authentication of signatures and the measurement of dissimilarities between signatures of the same and different individuals based on their patterns. The findings aid in identifying most influential parameter in distinguishing genuine signatures from counterfeits and enhance the evidential value of handwritten signature samples. Additionally, the simplified approach employed in this study allows for universal applicability across languages. Future research endeavours could expand the dataset and explore additional parameters to further improve the accuracy and reliability of signature verification methods in forensic analysis. [ABSTRACT FROM AUTHOR]

In this paper, we used the definition of fuzzy vector spaces and fuzzy subsets in combination in order to define fuzzy codes over a fuzzy vector space. We found that the combined definitions did not satisfy the definition of fuzzy vector spaces over a Galois field F2, and gave the conditions in which the combined definitions hold the definition of fuzzy vector spaces in order to define binary fuzzy codes. By defining binary fuzzy codes over fuzzy vector space in relation to the probability of binary symmetric channels (BSC) sending a codeword incorrectly and the weight of error pattern between codewords, we updated properties of Hamming distance of binary fuzzy codes over fuzzy vector space. From the updated properties, we found that the properties of Hamming distance stated over a vector space also satisfy in binary fuzzy codes defined over a fuzzy vector space. Furthermore, we found some interesting results on the decoding of fuzzy codewords sent over a BSC using minimum non-zero Hamming distance of binary fuzzy codes.

Spelling correction is a critical feature in software to reduce typing errors, commonly found in document processing software and smartphone keyboards. This research aims to evaluate the accuracy of the Hamming Distance method in correcting words in the Indonesian language, both standard and non-standard forms. The research data is derived from a previous study and comprises 60 standard and non-standard Indonesian words. Typos are generated by considering the layout of letters on the QWERTY keyboard. Typing error data is divided into two groups, namely words with 1 and 2 character differences. The first test is conducted on standard words, achieving an accuracy rate of 98.33% for 1 and 2 character differences. Subsequent testing on non-standard words shows an accuracy rate of 100% for 1 character difference and 96.67% for 2 character differences. The results of this research highlight the potential of the Hamming Distance method in improving the quality of spelling correction in the Indonesian language.

By constructing a complex network model grounded in time series analysis, this study delves into the intricate relationships between the stock markets of 18 countries worldwide. Utilizing 31 distinct time series distance functions to formulate the network, we employ Hamming distance to quantify the resemblance between networks derived from different distance functions. By modulating the network density through distance percentiles (p=0.1, 0.3, 0.5), we demonstrate the similarity of various distance functions across multiple density levels. Our findings reveal that certain distance functions exhibit high degrees of similarity across varying network densities, suggesting their potential for mutual substitution in network construction. Furthermore, the centroid network identified via hierarchical cluster analysis highlights the similarities between the stock markets of different nations, mirroring the intricate interconnections within the global financial landscape. The insights gained from this study offer crucial perspectives for comprehending the intricate network structure of global financial time series data, paving the way for further analysis and prediction of global financial market dynamics.

Introduction. The WHO regularly updates influenza vaccine recommendations to maximize their match with circulating strains. Nevertheless, the effectiveness of the influenza A vaccine, specifically its H3N2 component, has been low for several seasons. The aim of the study is to develop a mathematical model of cross-immunity based on the array of published WHO hemagglutination inhibition assay (HAI) data. Materials and methods. In this study, a mathematical model was proposed, based on finding, using regression analysis, the dependence of HAI titers on substitutions in antigenic sites of sequences. The computer program we developed can process data (GISAID, NCBI, etc.) and create real-time databases according to the set tasks. Results. Based on our research, an additional antigenic site F was identified. The difference in 1.6 times the adjusted R2, on subsets of viruses grown in cell culture and grown in chicken embryos, demonstrates the validity of our decision to divide the original data array by passage histories. We have introduced the concept of a degree of homology between two arbitrary strains, which takes the value of a function depending on the Hamming distance, and it has been shown that the regression results significantly depend on the choice of function. The provided analysis showed that the most significant antigenic sites are A, B, and E. The obtained results on predicted HAI titers showed a good enough result, comparable to similar work by our colleagues. Conclusion. The proposed method could serve as a useful tool for future forecasts, with further study to confirm its sustainability.

In this paper, we first use Hamming distance to mine students’ English achievement, construct the data matrix to get the English achievement, and analyze the standard deviation of each row and column in the matrix to get the similarity and the degree of dispersion among the students’ accomplishments. The BGLL algorithm is used to analyze achievement data by clustering. The modularity of the data calculated by clustering is applied to the weighted graph to obtain the results of achievement clustering analysis. By analyzing the effect of the fusion teaching mode of academic English and general English after practice, it was found that the English reading and speaking scores of the students in the experimental class improved significantly. There was a significant difference between the reading posttest scores of the experimental class and those of the control class (p =0.002

The rapid development of information technology has brought new opportunities to the field of language learning and formed new learning methods. In this paper, a content-based quantum recommendation algorithm is first proposed to calculate the preference attributes of English learners using the Hamming distance and then combined with the constructed dynamic interest model of learners to complete the personalized recommendation of English language learning resources. The algorithm is employed to build an English-language customized learning system that incorporates learning, resource management, and proficiency assessment functions, and performance testing is carried out simultaneously. Furthermore, the study utilizes the system to conduct application experiments to evaluate its improvement in English language learning efficiency. The results showed that the mean value of perceived satisfaction with the improvement of English language efficiency in classes using the system was 7.48, and the ANOVA results (F=3.88, p

The traditional feature-extraction method of oriented FAST and rotated BRIEF (ORB) detects image features based on a fixed threshold; however, ORB descriptors do not distinguish features well in capsule endoscopy images. Therefore, a new feature detector that uses a new method for setting thresholds, called the adaptive threshold FAST and FREAK in capsule endoscopy images (AFFCEI), is proposed. This method, first constructs an image pyramid and then calculates the thresholds of pixels based on the gray value contrast of all pixels in the local neighborhood of the image, to achieve adaptive image feature extraction in each layer of the pyramid. Subsequently, the features are expressed by the FREAK descriptor, which can enhance the discrimination of the features extracted from the stomach image. Finally, a refined matching is obtained by applying the grid-based motion statistics algorithm to the result of Hamming distance, whereby mismatches are rejected using the RANSAC algorithm. Compared with the ASIFT method, which previously had the best performance, the average running time of AFFCEI was 4/5 that of ASIFT, and the average matching score improved by 5% when tracking features in a moving capsule endoscope. [ABSTRACT FROM AUTHOR]

Ceramic art has a long history of development in China and is an important component of the inheritance and development of traditional culture. The highly distinctive ceramic art expression content has been sought after by many international research enthusiasts and applied to product design, highlighting different social backgrounds and special cultural connotations of different eras, while also laying an important theoretical foundation for the inheritance and development of culture. The quality evaluation of ceramic product modeling design could be deemed as a classic multiple attribute group decision making (MAGDM) problem. Recently, the EDAS and CRITIC method has been used to cope with MAGDM issues. Spherical fuzzy sets (SFSs) can excavate the uncertainty and fuzziness in MAGDM more effectively and deeply. In this article, on basis of evaluation based on distance from average solution (EDAS), a novel spherical fuzzy number EDAS (SFN-EDAS) method based on Hamming distance and Euclid distance is built for dealing with MAGDM. Moreover, when the attribute weights are completely unknown, the CRITIC method is extended to SFSs to acquire the attribute weights. Finally, SFN-EDAS method is used for quality evaluation of ceramic product modeling design to prove practicability of the developed method and compare SFN-EDAS method with existing methods to further demonstrate its superiority. [ABSTRACT FROM AUTHOR]

The energy consumption of mechanical products in China is enormous, and the energy utilization rate is low, which is increasingly receiving people's attention. Conducting product design for energy optimization is of great significance for improving energy utilization efficiency. The scheme design of a product is the key to achieving innovation in product design, and the evaluation and decision-making of the design scheme directly affect the results of the later stage of the design. Therefore, correctly evaluating and making reliable decisions on product design schemes that are oriented towards fuzzy decision optimization is an important aspect of product innovation conceptual design. The product modeling design quality evaluation is a multiple attribute group decision making (MAGDM) problems. Recently, the Combined Compromise Solution (CoCoSo) method and information entropy method has been employed to cope with MAGDM issues. The interval neutrosophic sets (INSs) are employed as a tool for portraying uncertain information during the product modeling design quality evaluation. In this paper, the CoCoSo method is designed for MAGDM under INSs. Then, the interval neutrosophic numbers CoCoSo (INN-CoCoSo) method based on the Hamming distance and Euclidean distance is built for MAGDM. The information Entropy method is employed to produce the weight information based on the Hamming distance and Euclidean distance under INNSs. Finally, a practical numerical example for product modeling design quality evaluation is supplied to show the INN-CoCoSo method. The main contributions of this paper are constructed: (1) This paper builds the novel MAGDM based on CoCoSo model under INSs; (2) The information Entropy method is employed to produce the weight information based on the Hamming distance and Euclidean distance under INNSs; (3) The new MAGDM method is proposed for product modeling design quality evaluation based on INN-CoCoSo. [ABSTRACT FROM AUTHOR]

With the proposal of the "Double First Class" construction concept in Chinese universities, cultivating high-quality talents has become the main direction of university education. At present, local universities in China are actively adapting to the changes of the times, taking the construction of the Double First Class as an opportunity, continuously adjusting and deepening the reform of the double innovation and innovation talent cultivation mode, further improving the curriculum system, increasing investment in professional practical teaching, stimulating students' enthusiasm for innovation and entrepreneurship (IAE), and focusing on improving students' comprehensive abilities, achieving significant results in talent cultivation. The "Entrepreneurship and Entrepreneurship" career ability evaluation of local college students could be considered as multiple attribute decision-making (MADM). Recently, the Combined Compromise Solution (CoCoSo) method and information entropy method was employed to deal with MADM. The triangular fuzzy neutrosophic sets (TFNSs) are employed as a better tool for expressing uncertain information during the "Entrepreneurship and Entrepreneurship" career ability evaluation of local college students. In this paper, the triangular fuzzy neutrosophic number CoCoSo (TFNN-CoCoSo) based on the Hamming distance and Euclid distance is constructed to cope with the MADM under TFNSs. The information entropy method is employed to compute the weight values based on the Hamming distance and Euclid distance under TFNSs. Finally, a numerical example of "Entrepreneurship and Entrepreneurship" career ability evaluation of local college students is constructed and some decision comparisons are constructed to verify the TFNN-CoCoSo method. [ABSTRACT FROM AUTHOR]

With the continuous development of China's economic system, the development of the construction industry is becoming more and more rapid, and the number and scale of construction projects are increasing. Due to the characteristics of large projects and long cycles, there are a large number of construction parties involved in construction projects. The increase in the number of participating partners makes it difficult for their projects to be integrated and managed by management departments such as owners, let alone for various parties to collaborate in the construction of projects. In order to effectively solve this problem, the engineering procurement construction (EPC) general contracting model has emerged. The risk assessment of EPC project is classical multiple attributes group decision making (MAGDM). The probabilistic hesitancy fuzzy sets (PHFSs) are used as a tool for characterizing uncertain information during the risk assessment of EPC project. In this paper, the classical grey relational analysis (GRA) method is extended to PHFSs. Firstly, the basic concept, comparative formula and Hamming distance of PHFSs are introduced. Then, the definition of the score values is employed to obtain the attribute weights based on the information entropy. Then, probabilistic hesitancy fuzzy GRA (PHF-GRA) method is built for MAGDM under PHFSs. Finally, a practical case study for risk assessment of EPC project is designed to validate the proposed method and some comparative studies are also designed to verify the applicability. [ABSTRACT FROM AUTHOR]

This paper presents a method for unsupervised classification of entities by a group of agents with unknown domains and levels of expertise. In contrast to the existing methods based on majority voting ("wisdom of the crowd") and their extensions by expectation-maximization procedures, the suggested method first determines the levels of the agents' expertise and then weights their opinions by their expertise level. In particular, we assume that agents will have relatively closer classifications in their field of expertise. Therefore, the expert agents are recognized by using a weighted Hamming distance between their classifications, and then the final classification of the group is determined from the agents' classifications by expectation-maximization techniques, with preference to the recognized experts. The algorithm was verified and tested on simulated and real-world datasets and benchmarked against known existing algorithms. We show that such a method reduces incorrect classifications and effectively solves the problem of unsupervised collaborative classification under uncertainty, while outperforming other known methods. [ABSTRACT FROM AUTHOR]

DNA code design is a challenging problem, and it has received great attention in the literature due to its applications in DNA data storage, DNA origami, and DNA computing. The primary focus of this paper is in constructing new DNA codes using the cosets of linear codes over the ring Z 4 . The Hamming distance constraint, GC-content constraint, and homopolymers constraint are all considered. In this study, we consider the cosets of Simplex alpha code, Kerdock code, Preparata code, and Hadamard code. New DNA codes of lengths four, eight, sixteen, and thirty-two are constructed using a combination of an algebraic coding approach and a variable neighborhood search approach. In addition, good lower bounds for DNA codes that satisfy important constraints have been successfully established using Magma software V2.24-4 and Python 3.10 programming in our comprehensive methodology. [ABSTRACT FROM AUTHOR]

The nonlinearity of vectorial functions and of their restrictions to manifolds are defined as the Hamming distance to the set of affine mappings and of their restrictions to the manifold, respectively. Relations between the parameters of the nonlinearity of a vectorial function and their analogues for its coordinate functions and its restrictions to manifolds are established. An analogue of the Parseval identity for such parameters of vectorial functions is proved, which implies the upper bound (qk − 1)qn−k − qn/2−k for the nonlinearity of a mapping over a q-element field of n variables with k coordinates. Attainability conditions for this estimate are found, and a class of Boolean vectorial functions with high value of nonlinearity is constructed. Estimates characterizing the distribution of the nonlinearity of a vectorial function and of its restrictions to manifolds are obtained. [ABSTRACT FROM AUTHOR]

This article presents the circular Pythagorean fuzzy set (C-PFS) model, a generalization of the circular intuitionistic fuzzy set model that improves its performance thanks to the acclaimed extension of intuitionistic fuzzy sets to Pythagorean fuzzy sets. Then, we generalize C-PFSs to produce the novel disc Pythagorean fuzzy sets (D-PFSs). The constituent elements of both C-PFSs and D-PFSs are circular Pythagorean fuzzy values, either with a common or a distinctive radius. We lay out some fundamental algebraic and arithmetic operations on D-PFSs (hence on C-PFSs), namely union, intersection, addition, multiplication, and scalar multiplication, and we explore the main features of these operations. We propose and investigate the properties of the novel circular Pythagorean fuzzy weighted average/geometric aggregation operators. The "COmbinative Distance based ASsesment" approach, which is based on the Hamming and Euclidean distances, is expanded to the D-PFS framework. To justify its implementability, we apply the new methodology to a case study (selection of the best supermarkets to buy fresh fruit for a hotel) and then we compare it to related solutions. [ABSTRACT FROM AUTHOR]

A family of sets A$A$ is said to be an antichain if x⊄y$x\not\subset y$ for all distinct x,y∈A$x,y\in A$, and it is said to be a distance‐r$r$ code if every pair of distinct elements of A$A$ has Hamming distance at least r$r$. Here, we prove that if A⊂2[n]$A\subset 2^{[n]}$ is both an antichain and a distance‐r$r$ code, then |A|=Or(2nn−1/2−⌊(r−1)/2⌋)$|A| = O_r(2^n n^{-1/2 - \lfloor (r-1)/2\rfloor })$. This result, which is best‐possible up to the implied constant, is a purely combinatorial strengthening of a number of results in Littlewood–Offord theory; for example, our result gives a short combinatorial proof of Hálasz's theorem, while all previously known proofs of this result are Fourier‐analytic. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Maximally asymmetric functions are multiple-valued functions that have the maximum distance from their nearest symmetric functions, in terms of Hamming distance. Maximally asymmetric functions can be promising for applications such as radio communication and cryptography, due to randomness of the functions. To promote such application studies, benchmarks for maximally asymmetric functions in compact form are needed. However, few studies on benchmark generation or even on characteristics for maximally asymmetric functions have been reported. Thus, this paper investigates representation of maximally asymmetric functions based on decision diagrams. This paper begins with proposing a method to compute asymmetry of a given function easily, and then, derives a new characteristic of maximally asymmetric functions based on the computation method. Using the derived characteristic, we also propose a method to automatically generate benchmarks for maximally asymmetric functions represented by decision diagrams. By comparing sizes of different types of decision diagrams, we consider suitable representations for maximally asymmetric functions. [ABSTRACT FROM AUTHOR]

A Bayesian network (BN) is a probabilistic graphical model that can model complex and nonlinear relationships. Its structural learning from data is an NP-hard problem because of its search-space size. One method to perform structural learning is a search and score approach, which uses a search algorithm and structural score. A study comparing 15 algorithms showed that hill climbing (HC) and tabu search (TABU) performed the best overall on the tests. This work performs a deeper analysis of the application of the adaptive genetic algorithm with varying population size (AGAVaPS) on the BN structural learning problem, which a preliminary test showed that it had the potential to perform well on. AGAVaPS is a genetic algorithm that uses the concept of life, where each solution is in the population for a number of iterations. Each individual also has its own mutation rate, and there is a small probability of undergoing mutation twice. Parameter analysis of AGAVaPS in BN structural leaning was performed. Also, AGAVaPS was compared to HC and TABU for six literature datasets considering F1 score, structural Hamming distance (SHD), balanced scoring function (BSF), Bayesian information criterion (BIC), and execution time. HC and TABU performed basically the same for all the tests made. AGAVaPS performed better than the other algorithms for F1 score, SHD, and BIC, showing that it can perform well and is a good choice for BN structural learning. [ABSTRACT FROM AUTHOR]

We are coming across cases, where the education system is being distorted and perverted. As the present security of our education system has several loopholes that can be exploited to obtain access to the locations were question papers are being kept, posing a significant threat to society, to address this problem we engineered an intrusion detection recording system with a biometric lock. Our project is a result of combination of two already existing methodologies - Wireless Biometric Lock and Noise detector with automatic recording system. This combination provides us with a more secure system than the existing individual implementation. Thus, our project is a noise sensor-based device with an automatic recording system that can also lock the locker or door using a fingerprint-based biometric interface which not only detects intrusion but also traps the person who tries to intrude. [ABSTRACT FROM AUTHOR]

In this research article, an improved Multi-criteria group decision-making (MCGDM) strategy has been developed in pentagonal neutrosophic environment incorporating grey relational analysis and method on the removal effects of criteria (MEREC) techniques to address the relative advantages and disadvantages of these aspects in MCGDM. The aim of the study is to improve MCGDM technique which can capture the underlying uncertainties in robust way and can produce consistent results in a more rigorous way. Here, the conception of Hamming distance between two pentagonal neutrosophic number (PNN)s is introduced and the weighted arithmetic and geometric averaging operators in PNN arena are deployed to craft our computational technique more progressive and robust. An agriculture-based numerical problem is illustrated to demonstrate the ranking results of the alternatives by both of the techniques. After evaluating the problem by two aggregation operators, it is found that "plantation crop" is the best alternative under certain circumstances. Lastly, the sensitivity investigation is performed which reveals that with the appliance of arithmetic and geometric aggregation operators the best ranked alternative preserves its position by both of the ranking methods, which definitely exhibit the consistency and robustness of our executed methodology. [ABSTRACT FROM AUTHOR]

Objectives: Specifiers for a major depressive disorder (MDE) are supposed to reduce diagnostic heterogeneity. However, recent literature challenges the idea that the atypical and melancholic specifiers identify more homogenous or coherent subgroups. We introduce the usage of distance metrics to characterize symptom heterogeneity. We attempt to replicate prior findings and explore whether symptom heterogeneity is reduced using specifier subgroups. Methods: We used data derived from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC Wave I; N = 5,749) and the Sequenced Treatment Alternatives to Relieve Depression study (STAR*D; N = 2,498). We computed Hamming and Manhattan distances from study participants' unique symptom profiles. Distances were standardized from 0-1 and compared by their within- and between-group similarities to their non-specifier counterparts for the melancholic and atypical specifiers. Results: There was no evidence of statistically significant differences in heterogeneity for specifier (i.e., melancholic or atypical) vs. non-specifier designations (i.e., non-melancholic vs. non-atypical). Conclusion: Replicating prior work, melancholic and atypical depression specifiers appear to have limited utility in reducing heterogeneity. The current study does not support the claim that specifiers create more coherent subgroups as operationalized by similarity in the number of symptoms and their severity. Distance metrics are useful for quantifying symptom heterogeneity. [ABSTRACT FROM AUTHOR]

In this work, we explored a highly robust and unique Physical Unclonable Function (PUF) based on the stochastic assembly of single-walled Carbon NanoTubes (CNTs) integrated within a wafer-level technology. Our work demonstrated that the proposed CNT-based PUFs are exceptionally robust with an average fractional intra-device Hamming distance well below 0.01 both at room temperature and under varying temperatures in the range from 23 ∘ C to 120 ∘ C . We attributed the excellent heat tolerance to comparatively low activation energies of less than 40 meV extracted from an Arrhenius plot. As the number of unstable bits in the examined implementation is extremely low, our devices allow for a lightweight and simple error correction, just by selecting stable cells, thereby diminishing the need for complex error correction. Through a significant number of tests, we demonstrated the capability of novel nanomaterial devices to serve as highly efficient hardware security primitives. [ABSTRACT FROM AUTHOR]

In this paper we show that the largest possible size of a subset of F q n avoiding right angles, that is, distinct vectors x , y , z such that x − z and y − z are perpendicular to each other is at most O (n q − 2). This improves on the previously best known bound due to Naslund [13] and refutes a conjecture of Ge and Shangguan [11]. A lower bound of n q / 3 is also presented. It is also shown that a subset of F q n avoiding triangles with all right angles can have size at most O (n 2 q − 2). Furthermore, asymptotically tight bounds are given for the largest possible size of a subset A ⊆ F q n for which x − y is not self-orthogonal for any distinct x , y ∈ A. The exact answer is determined for q = 3 and n ≡ 2 (mod 3). Our methods can also be used to bound the maximum possible size of a binary code where no two codewords have Hamming distance divisible by a fixed prime q. Our lower- and upper bounds are asymptotically tight and both are sharp in infinitely many cases. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For any odd prime p ≠ 5 , the structures of cyclic codes of length 5 p s over F p m are applied to construct quantum error-correcting codes (briefly, QEC codes). Some new QEC codes are provided in the sense that their parameters are different from all the previous constructions. We give all quantum maximum-distance-separable (briefly, qMDS codes) constructed by the CSS construction. We also construct quantum synchronizable codes (briefly, QSCs). To enrich the variety of available QSCs, many new QSCs are constructed to illustrate our results. Among them, there are QSCs codes with shorter lengths and much larger minimum distances than known primitive narrow-sense BCH codes. [ABSTRACT FROM AUTHOR]

Let F ⊆ 2 [ n ] be a fixed family of subsets. Let D (F) stand for the following set of Hamming distances: D (F) : = { d H (F , G) : F , G ∈ F , F ≠ G } . F is said to be a Hamming symmetric family, if F X implies n - d ∈ D (F) for each d ∈ D (F) . We give sharp upper bounds for the size of Hamming symmetric families. Our proof is based on the linear algebra bound method. [ABSTRACT FROM AUTHOR]

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In visual simultaneous localization and mapping (SLAM) systems, the limitations of the assumption of scene rigidity are usually broken by using learning-based or geometry-based methods. However, learning-based methods usually have a high time cost, and geometry-based methods usually do not result in clean maps which are useful for advanced robotic applications. In this paper, an RGB-D SLAM in indoor dynamic environments with two channels that classifies frames as slightly and highly dynamic scenarios based on matching accuracy is proposed. And a geometric constraint based on Hamming distance is proposed to improve the effectiveness of matching accuracy as a basis for scenario classification. Dynamic features are detected by affine consistency constraint and semantic method. The semantic method is only used for highly dynamic scenarios to reduce the time cost of dynamic feature detection and provide a basis for mapping. Furthermore, an improved adaptive threshold algorithm is proposed to improve the robustness of feature matching. The proposed method is evaluated in the TUM RGB-D dataset and a real scenario. The experimental results demonstrate that the proposed method achieves highly accurate tracking with appreciable time cost in both slightly and highly indoor dynamic environments while obtaining effective maps. [ABSTRACT FROM AUTHOR]