Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this article.Adescription is provided of the scoring function,accuracy function and operational laws of the log q-rung VS.The algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming distance.Additionally,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify conceptualization.We chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile amnesia.Natural numbers q are used to express the results of the models.To demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

Inverse Generalized Minimum Cost Flow Problem Under the Hamming Distances

Given a generalized minimum cost flow problem,the corresponding inverse problem is to find a minimal adjustment of the cost function so that the given generalized flow becomes optimal to the problem.In this paper,we consider both types of the weighted Hamming distances for measuring the adjustment.In the sum-type case,it is shown that the inverse problem is APX-hard.In the bottleneck-type case,we present a polynomial time algorithm.

ON THE EXPECTATION AND VARIANCE OF HAMMING DISTANCE BETWEEN TWO I.I.D RANDOM VECTORS

By using the generalized MacWilliams theorem, we give new representations for expectation and variance of Hamming distance between two i.i.d random vectors. By using the new representations, we derive a lower bound for the variance, and present a simple and direct proof of the inequality of [1].

A Unified Bit-to-Symbol Mapping for Generalized Constellation Modulation

Gray mapping is a well-known way to improve the performance of regular constellation modulation,but it is challenging to be applied directly for irregular alternative.To address this issue,in this paper,a unified bit-to-symbol mapping method is designed for generalized constellation modulation(i.e.,regular and irregular shaping).The objective of the proposed approach is to minimize the average bit error probability by reducing the hamming distance(HD)of symbols with larger values of pairwise error probability.Simulation results show that the conventional constellation modulation(i.e.,phase shift keying and quadrature amplitude modulation(QAM)with the proposed mapping rule yield the same performance as that of classical gray mapping.Moreover,the recently developed golden angle modulation(GAM)with the proposed mapping method is capable of providing around1 d B gain over the conventional mapping counterpart and offers comparable performance to QAM with Gray mapping.

改进模板匹配的快速识别算法

为解决噪声导致的模板图像误匹配问题以及提高算法的识别速度和准确率,提出一种改进模板匹配的快速识别算法。该算法使用改进的低通滤波器对图像进行降噪处理,并使用改进的加权Hamming Distance,对图像进行相似度检测,降低算法的复杂程度,最后使用阈值和同伦骨架加快算法识别的运行速度。通过对不同背景下的噪声图像进行实验,该算法在目标识别过程中有很好的识别效果,对噪声有很好的鲁棒性,并且能够改善算法的运行效率,从而证明了该算法在目标识别过程中的有效性。

Random-Like Testing of Very Large Scale Integration Circuit

A new approach to improve the test efficiency of random testing is presented in this paper. In conventional random testing, each test pattern is selected randomly regardless of the tests previously generated. This paper introduces the concept of random like testing. The method provided appears to have the same concepts as used in random testing,but actually takes an opposite way to it in order to improve the efficiency of random testing.In a random like testing sequence, the total distance among all test patterns is chosen to be maximal so that the fault sets detected by one test pattern are as different as possible from that detected by the tests previously applied. The procedure to construct a random like testing sequence (RLTS) is described in detail. Theorems to justify the effectiveness and usefulness of the procedure presented are developed. Experimental results on benchmark circuits as well as on other circuit are also given to evaluate the performance of the new approach.

Hamming-distance-based adaptive quantum-inspired evolutionary algorithm for network coding resources optimization

An adaptive quantum-inspired evolutionary algorithm based on Hamming distance （HD-QEA） was presented to optimize the network coding resources in multicast networks. In the HD-QEA, the diversity among individuals was taken into consideration, and a suitable rotation angle step （RAS） was assigned to each individual according to the Hamming distance. Performance comparisons were conducted among the HD-QEA, a basic quantum-inspired evolutionary algorithm （QEA） and an individual＇s fitness based adaptive QEA. A solid demonstration was provided that the proposed HD-QEA is better than the other two algorithms in terms of the convergence speed and the global optimization capability when they are employed to optimize the network coding resources in multicast networks.

CONSTRUCTING UNIFORM DESIGNS WITH TWO- OR THREE-LEVEL

When the number of runs is large, to search for uniform designs in the sense of low-discrepancy is an NP hard problem. The number of runs of most of the available uniform designs is small （≤50）. In this article, the authors employ a kind of the so-called Hamming distance method to construct uniform designs with two- or three-level such that some resulting uniform designs have a large number of runs. Several infinite classes for the existence of uniform designs with the same Hamming distances between any distinct rows are also obtained simultaneously. Two measures of uniformity, the centered L2-discrepancy （CD, for short） and wrap-around L2-discrepancy （WD, for short）, are employed.

Novel Ransomware Hiding Model Using HEVC Steganography Approach

Ransomware is considered one of the most threatening cyberattacks.Existing solutions have focused mainly on discriminating ransomware by analyzing the apps themselves,but they have overlooked possible ways of hiding ransomware apps and making them difficult to be detected and then analyzed.Therefore,this paper proposes a novel ransomware hiding model by utilizing a block-based High-Efficiency Video Coding(HEVC)steganography approach.The main idea of the proposed steganography approach is the division of the secret ransomware data and cover HEVC frames into different blocks.After that,the Least Significant Bit(LSB)based Hamming Distance(HD)calculation is performed amongst the secret data’s divided blocks and cover frames.Finally,the secret data bits are hidden into the marked bits of the cover HEVC frame-blocks based on the calculated HD value.The main advantage of the suggested steganography approach is the minor impact on the cover HEVC frames after embedding the ransomware while preserving the histogram attributes of the cover video frame with a high imperceptibility.This is due to the utilization of an adaptive steganography cost function during the embedding process.The proposed ransomware hiding approach was heavily examined using subjective and objective tests and applying different HEVC streams with diverse resolutions and different secret ransomware apps of various sizes.The obtained results prove the efficiency of the proposed steganography approach by achieving high capacity and successful embedding process while ensuring the hidden ransomware’s undetectability within the video frames.For example,in terms of embedding quality,the proposed model achieved a high peak signal-to-noise ratio that reached 59.3 dB and a low mean-square-error of 0.07 for the examined HEVC streams.Also,out of 65 antivirus engines,no engine could detect the existence of the embedded ransomware app.

Novel Mapping Design Criterion for BICM-ID with Square QAM Constellations

Mapping design criteria of bit-interleaved coded modulation with iterative decoding (BICM-ID) with square 16QAM are analyzed. Three of the existing criteria are analyzed and compared with each other. Through the comparison, two main characters of the mapping design criteria are found. They are the harmonic mean of the minimum squared Euclidean distance and the average of Hamming distances with the nearest Euclidean distance. Based on these two characters, a novel mapping design criterion is proposed and a label mapping named mixed mapping is searched according to it. Simulation results show that mixed mapping performs better than the other mappings in BICM-ID system.

Double-pattern associative memory neural network with pattern loop

A double-pattern associative memory neural network with “pattern loop” is proposed. It can store 2N bit bipolar binary patterns up to the order of 2 2N , retrieve part or all of the stored patterns which all have the minimum Hamming distance with input pattern, completely eliminate spurious patterns, and has higher storing efficiency and reliability than conventional associative memory. The length of a pattern stored in this associative memory can be easily extended from 2N to kN.

Achieving Privacy-Preserving Iris Identification Via El Gamal

Currently,many biometric systems maintain the user’s biometrics and templates in plaintext format,which brings great privacy risk to uses’biometric information.Biometrics are unique and almost unchangeable,which means it is a great concern for users on whether their biometric information would be leaked.To address this issue,this paper proposes a confidential comparison algorithm for iris feature vectors with masks,and develops a privacy-preserving iris verification scheme based on the El Gamal encryption scheme.In our scheme,the multiplicative homomorphism of encrypted features is used to compare of iris features and their mask information.Also,this paper improves the Hamming distance of iris features,which makes the similarity matching work better than existing ones.Experimental results confirm the practicality of our proposed schemes in real world applications,that is,for the iris feature vectors and masks of 2048 bits,nearly 12 comparisons can be performed per second.

An Exploration on the Applicability of Several Statistical Similarity Methods

In allusion to the previous various forecasting verification results of each similarity measurement, starting from formulas, the fact that there existed the same features between analog deviation and Hamming distance was analyzed and then proved. The limitations of analog deviation used in similarity forecast were discussed. The data from 96 different stations at 850 hPa in the East Asia during May 1 -30, 2010 were utilized to make similarity selection experiments among several frequently used similarity measurements and the new one proposed by the authors of this essay. The result indicated that analog deviation and Hamming distance were very much similar, with over 80% selected samples being the same. Analog deviation had the biggest difference with similarity coefficient, with over 70% selected samples being different. The new analog quantity was more similar to the related coefficient, with 60% selected samples being the same. Analog deviation and Hamming distance reflected how much the distances of the samples were similar to each other, while related coefficient and the new analog quantity reflected how much the shapes of the selected samples were similar to each other.

Interval-Valued Intuitionistic Fuzzy-Rough Sets

This paper combines interval-valued intuitionistic fuzzy sets and rough sets.It studies rougheness in interval-valued intuitionistic fuzzy sets and proposes one kind of interval-valued intuitionistic fuzzy-rough sets models under the equivalence relation in crisp sets.That extends the classical rough set defined by Pawlak.

Algebra and Geometry of Sets in Boolean Space

In the present paper, geometry of the Boolean space Bn in terms of Hausdorff distances between subsets and subset sums is investigated. The main results are the algebraic and analytical expressions for representing of classical figures in Bn and the functions of distances between them. In particular, equations in sets are considered and their interpretations in combinatory terms are given.

An Adjusted Gray Map Technique for Constructing Large Four-Level Uniform Designs

A uniform experimental design(UED)is an extremely used powerful and efficient methodology for designing experiments with high-dimensional inputs,limited resources and unknown underlying models.A UED enjoys the following two significant advantages:(i)It is a robust design,since it does not require to specify a model before experimenters conduct their experiments;and(ii)it provides uniformly scatter design points in the experimental domain,thus it gives a good representation of this domain with fewer experimental trials(runs).Many real-life experiments involve hundreds or thousands of active factors and thus large UEDs are needed.Constructing large UEDs using the existing techniques is an NP-hard problem,an extremely time-consuming heuristic search process and a satisfactory result is not guaranteed.This paper presents a new effective and easy technique,adjusted Gray map technique(AGMT),for constructing(nearly)UEDs with large numbers of four-level factors and runs by converting designs with s two-level factors and n runs to(nearly)UEDs with 2^(t−1)s four-level factors and 2tn runs for any t≥0 using two simple transformation functions.Theoretical justifications for the uniformity of the resulting four-level designs are given,which provide some necessary and/or sufficient conditions for obtaining(nearly)uniform four-level designs.The results show that the AGMT is much easier and better than the existing widely used techniques and it can be effectively used to simply generate new recommended large(nearly)UEDs with four-level factors.

Two-Stage Constructions for the Rate-Compatible Shortened Polar Codes

In this paper,we propose the two-stage constructions for the rate-compatible shortened polar(RCSP)codes.For the Stage-I construction,the shortening pattern and the frozen bit are jointly designed to make the shortened bits be completely known by the decoder.Besides,a distance-greedy algorithm is presented to improve the minimum Hamming distance of the codes.To design the remaining Stage-II frozen bits,three different construction algorithms are further presented,called the Reed-Muller(RM)construction,the Gaussian Approximation(GA)construction,and the RM-GA construction.Then we give the row weight distribution numerical results of the generator matrix after the Stage-I and Stage-II constructions,which shows that the proposed constructions can efficiently increase the minimum Hamming distance.Simulation results show that the proposed RCSP codes have excellent frame error rate(FER)performances at different code lengths and code rates.More specifically,the RM-GA construction performs best and can achieve at most 0.8 dB gain compared to the Wang14 and the quasi-uniform puncturing(QUP)schemes.The RM construction is designed completely by the distance-constraint without channel evaluation thus has the simplest structure.Interestingly,it still has better FER performance than the existing shortening/puncturing schemes,especially at high signal noise ratio(SNR)region.

Classifying and clustering in negative databases

Recently, negative databases （NDBs） are proposed for privacy protection. Similar to the traditional databases, some basic operations could be conducted over the NDBs, such as select, intersection, update, delete and so on. However, both classifying and clustering in negative databases have not yet been studied. Therefore, two algorithms, i.e., a k nearest neighbor （kNN） classification algorithm and a k-means clustering algorithm in NDBs, are proposed in this paper, respectively. The core of these two algorithms is a novel method for estimating the Hamming distance between a binary string and an NDB. Experimental results demonstrate that classifying and clustering in NDBs are promising.

A General Pairwise Key Predistribution Scheme for Wireless Sensor Networks

This paper develops a general hypercube-based key predistribution scheme for establishing pairwise keys between sensor nodes using polynomials, which is parameterized by the dimension of hypercube and the Hamming distance threshold variables. The scheme addresses the weaknesses of existing key predistribution schemes, which have either worse security or lower efficiency. It exhibits a nice property--when the Hamming distance between any two neighboring sensor nodes is less than the pre-defined threshold, the pairwise key can be established directly. Extensive performance and security analysis shows that by increasing Hamming distance threshold value, we can trade off the resilience against node capture attack for higher probability of direct pairwise key establishment, so as to save the energy consumption which is the most important issue for sensor networks.

Cross-Entropy Loss for Recommending Efficient Fold-Over Technique

Due to the limited resources and budgets in many real-life projects, it is unaffordable to use full factorial experimental designs and thus fractional factorial(FF) designs are used instead. The aliasing of factorial effects is the price we pay for using FF designs and thus some significant effects cannot be estimated. Therefore, some additional observations(runs) are needed to break the linages among the factorial effects. Folding over the initial FF designs is one of the significant approaches for selecting the additional runs. This paper gives an in-depth look at fold-over techniques via the following four significant contributions. The first contribution is on discussing the adjusted switching levels foldover technique to overcome the limitation of the classical one. The second contribution is on presenting a comparison study among the widely used fold-over techniques to help experimenters to recommend a suitable fold-over technique for their experiments by answering the following two fundamental questions:Do these techniques dramatically lessen the confounding of the initial designs, and do the resulting combined designs(combining initial design with its fold-over) via these techniques have considerable difference from the optimality point of view considering the markedly different searching domains in each technique? The optimality criteria are the aberration, confounding, Hamming distance and uniformity. Many of these criteria are given in sequences(patterns) form, which are inconvenient and costly to represent and compare, especially when the designs have many factors. The third innovation is on developing a new criterion(dictionary cross-entropy loss) to simplify the existing criteria fromsequence to scalar. The new criterion leads to a more straightforward and easy comparison study. The final contribution is on establishing a general framework for the connections between initial designs and combined designs based on any fold-over technique.