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The problem of classifying constacyclic codes over a finite field, both the Hamming distance and the algebraic structure, is an interesting problem in algebraic coding theory. For the repeated-root constacyclic codes of length n p s over p m , where p is a prime number and p does not divide n , the problem has been solved completely for all n ≤ 6 and partially for n = 7 , 8. In this paper, we solve the problem for n = 9 and all primes p different from 3 and 1 9. In particular, we characterize the Hamming distance of all repeated-root constacyclic codes of length 9 p s over p m . As an application, we identify all optimal and near-optimal codes with respect to the Singleton bound of these types, namely, MDS, almost-MDS, and near-MDS codes. [ABSTRACT FROM AUTHOR]

Physical unclonable functions (PUFs) have emerged as a promising encryption technology, utilizing intrinsic physical identifiers that offer enhanced security and tamper resistance. Multi‐level PUFs boost system complexity, thereby improving system reliability and fault tolerance. However, crosstalk‐free multi‐level PUFs remain a persistent challenge. In this study, a hierarchical PUF system that harnesses the spontaneous phase separation of silk fibroin /PVA blend and the random distribution of silicon‐vacancy diamonds within the blend is presented. The thermodynamic instability of phase separation and inherent unpredictability of diamond dispersion gives rise to intricate random patterns at two distinct scales, enabling time‐efficient hierarchical authentication for cryptographic keys. These patterns are complementary yet independent, inherently resistant to replication and damage thus affording robust security and reliability to the proposed system. Furthermore, customized authentication algorithms are constructed: visual PUFs authentication utilizes neural network combined structural similarity index measure, while spectral PUFs authentication employs Hamming distance and cross‐correlation bit operation. This hierarchical PUF system attains a high recognition rate without interscale crosstalk. Additionally, the coding capacity is exponentially enhanced using M‐ary encoding to reinforce multi‐level encryption. Hierarchical PUFs hold significant potential for immediate application, offering unprecedented data protection and cryptographic key authentication capabilities. [ABSTRACT FROM AUTHOR]

The snake optimizer (SO) is a highly efficient bio-inspired algorithm for solving continuous optimization problems. This algorithm mathematically simulates the unique foraging and mating behaviors observed in snake populations in nature. However, this algorithm cannot be directly applied to solve binary optimization problems, such as feature selection. Feature selection is a significant data preprocessing step in data mining, aimed at reducing data dimensionality, lowering computational costs in terms of time and space, and improving the predictive accuracy of classifiers. To address this limitation, an improved binary version of SO (IBSO) is proposed for feature selection, which incorporates the concept of hamming distance and introduces a novel mutation transfer function (MTF). IBSO extends the application of the conventional SO to binary optimization problems by introducing hamming distance and presents a new binary position update strategy. Furthermore, IBSO utilizes a MTF based on a Gaussian distribution. The MTF not only transforms each dimension of each individual in the population into binary space but also enhances the local random search capability and increases the population diversity of the algorithm. Finally, the experiment is conducted on 27 standard benchmark datasets from UC Irvine Machine Learning Repository and IBSO is compared with several state-of-the-art binary swarm intelligence algorithms to analyze the effectiveness and efficiency of IBSO. The results show that IBSO can obtain the best fitness values with less CPU time. Besides, to evaluate the validity of the proposed MTF, IBSO is compared with other binary versions of SO with different well-known transfer functions. [ABSTRACT FROM AUTHOR]

The so-called Hamming distance measures the difference between two binary strings A and B. In simplified form, it measures the number of changes in A to get B. This type of distance is very useful in classical computing in applications such as error correction. It is also advantageous in quantum computing, being for example widely used in quantum machine learning. Since current quantum computers have limited resources, this type of distance is particularly attractive because it can be computed using fewer qubits and operations than other distances such as Euclidean or Manhattan distances. In this paper, two circuits for calculating Hamming distances using exclusively Clifford+T gates are presented. The aim of both circuits is to reduce the quantum cost and number of T gates needed to compute the Hamming distance. The T gate is more expensive than the other gates, so this reduction will have a significant impact on the total cost of the circuits. Furthermore, the proposed circuits are implemented using only Clifford+T gates. The circuits implemented exclusively with this group of gates are compatible with proven error detection and correction codes. [ABSTRACT FROM AUTHOR]

Background: Modeling causality through graphs, referred to as causal graph learning, offers an appropriate description of the dynamics of causality. The majority of current machine learning models in clinical decision support systems only predict associations between variables, whereas causal graph learning models causality dynamics through graphs. However, building personalized causal graphs for each individual is challenging due to the limited amount of data available for each patient. Method: In this study, we present a new algorithmic framework using meta-learning for learning personalized causal graphs in biomedicine. Our framework extracts common patterns from multiple patient graphs and applies this information to develop individualized graphs. In multi-task causal graph learning, the proposed optimized initial guess of shared commonality enables the rapid adoption of knowledge to new tasks for efficient causal graph learning. Results: Experiments on one real-world biomedical causal graph learning benchmark data and four synthetic benchmarks show that our algorithm outperformed the baseline methods. Our algorithm can better understand the underlying patterns in the data, leading to more accurate predictions of the causal graph. Specifically, we reduce the structural hamming distance by 50-75%, indicating an improvement in graph prediction accuracy. Additionally, the false discovery rate is decreased by 20-30%, demonstrating that our algorithm made fewer incorrect predictions compared to the baseline algorithms. Conclusion: To the best of our knowledge, this is the first study to demonstrate the effectiveness of meta-learning in personalized causal graph learning and cause inference modeling for biomedicine. In addition, the proposed algorithm can also be generalized to transnational research areas where integrated analysis is necessary for various distributions of datasets, including different clinical institutions. [ABSTRACT FROM AUTHOR]

In communication technologies such as digital watermarking, wireless sensor networks (WSNs), and visual light communication (VLC), error-correcting codes are crucial. The Enhanced Hadamard Error-Correcting Code (EHC), which is based on 2D Hadamard Basis Images, is a novel error correction technique that is presented in this study. This technique is used to evaluate the effectiveness of the video watermarking scheme. Even with highly sophisticated embedding techniques, watermarks usually fail to resist such comprehensive attacks because of the extraordinarily high compression rate of approximately 1:200 that is frequently employed in video dissemination. It can only be used in conjunction with a sufficient error-correcting coding method. This study compares the efficacy of the well-known Reed–Solomon Code with this novel technique, the Enhanced Hadamard Error-Correcting Code (EHC), in maintaining watermarks in embedded videos. The main idea behind this newly created multidimensional Enhanced Hadamard Error-Correcting Code is to use a 1D Hadamard decoding approach on the 2D base pictures after they have been transformed into a collection of one-dimensional rows. Following that, the image is rebuilt, allowing for a more effective 2D decoding procedure. Using this technique, it is possible to exceed the theoretical error-correcting capacity threshold of ⌊ d m i n − 1 2 ⌋ bits, where d m i n is the Hamming distance. It may be possible to achieve better results by converting the 2D EHC into a 3D format. The new Enhanced Hadamard Code is used in a video watermarking coding scheme to show its viability and efficacy. The original video is broken down using a multi-level interframe wavelet transform during the video watermarking embedding process. Low-pass filtering is applied to the video stream in order to extract a certain frequency range. The watermark is subsequently incorporated using this filtered section. Either the Reed–Solomon Correcting Code or the Enhanced Hadamard Code is used to protect the watermarks. The experimental results show that EHC far outperforms the RS Code and is very resilient against severe MPEG compression. [ABSTRACT FROM AUTHOR]

The three-way decision paradigm is a decision-making approach that is analogous to human reasoning. It involves a neutrosophic set as an efficient tool for handling ambiguity. Using cotangent similarity, the Hamming distance, and the innovative Vicis-Wave Hedges distance for ranking, this study proposes three novel three-way decision models based on the Bayes decision. In particular, single-valued neutrosophic numbers are employed to represent each loss function. The proposed models are then condensed into an algorithm. Subsequently, an example is presented to demonstrate the validity and rationality of the proposed models. Finally, we compare the three models. This research provides a new method for decision-making problems. [ABSTRACT FROM AUTHOR]

Secure hamming distance computation is an important primitive of secure multiparty computation, enabling two parties to jointly compute the hamming distance without revealing any private information of each party. Secure hamming distance computation has a wide application in private similarity determination fields, such as secure biometric identification and error detection. In this paper, we present a quantum protocol for secure hamming distance computation based on single photons and local unitary operations, ensuring easy implementation with present technologies. Our protocol guarantees fairness between the two parties and provides higher security compared to classical protocols, leveraging the physical principles of quantum mechanics. [ABSTRACT FROM AUTHOR]

In 2018, Luca and Patel conjectured that the largest perfect power representable as the sum of two Fibonacci numbers is 38642 = F36 + F12. In other words, they conjectured that the equation has no solutions with a ≥ 2 and ya > 38642 While this is still an open problem, there exist several partial results. For example, recently Kebli, Kihel, Larone and Luca proved an explicit upper bound for ya, which depends on the size of y. In this paper, we find an explicit upper bound for ya, which only depends on the Hamming weight of y with respect to the Zeckendorf representation. More specifically we prove the following: If y = Fn1 + · · · + Fnk and equation (*) is satisfied by y and some non-negative integers n, m and a ≥ 2, then Here, ϵ > 0 can be chosen arbitrarily and C(ϵ) is an effectively computable constant. [ABSTRACT FROM AUTHOR]

Minimizers and convolutional neural networks (CNNs) are two quite distinct popular techniques that have both been employed to analyze categorical biological sequences. At face value, the methods seem entirely dissimilar. Minimizers use min-wise hashing on a rolling window to extract a single important k-mer feature per window. CNNs start with a wide array of randomly initialized convolutional filters, paired with a pooling operation, and then multiple additional neural layers to learn both the filters themselves and how they can be used to classify the sequence. In this study, our main result is a careful mathematical analysis of hash function properties showing that for sequences over a categorical alphabet, random Gaussian initialization of convolutional filters with max-pooling is equivalent to choosing a minimizer ordering such that selected k-mers are (in Hamming distance) far from the k-mers within the sequence but close to other minimizers. In empirical experiments, we find that this property manifests as decreased density in repetitive regions, both in simulation and on real human telomeres. We additionally train from scratch a CNN embedding of synthetic short-reads from the SARS-CoV-2 genome into 3D Euclidean space that locally recapitulates the linear sequence distance of the read origins, a modest step toward building a deep learning assembler, although it is at present too slow to be practical. In total, this article provides a partial explanation for the effectiveness of CNNs in categorical sequence analysis. [ABSTRACT FROM AUTHOR]

Quantification of chaos is a challenging issue in complex dynamical systems. In this paper, we discuss the chaotic properties of generalized Lotka–Volterra and May–Leonard models of biodiversity, via the Hamming distance density. We identified chaotic behavior for different scenarios via the specific features of the Hamming distance and the method of q-exponential fitting. We also investigated the spatial autocorrelation length to find the corresponding characteristic length in terms of the number of species in each system. In particular, the results concerning the characteristic length are in good accordance with the study of the chaotic behavior implemented in this work. [ABSTRACT FROM AUTHOR]

The multilevel encoding (MLE) scheme is an effective method for improving the anticounterfeiting encryption capabilities of physically unclonable functions (PUFs). However, owing to the correlation between encoding layers, the encoding capacity (EC) is difficult to improve by orders of magnitude. Herein, four noncorrelated structures in the diamond crystal structure (carbon–carbon single bond, defect luminescence structures, spin structures, and electron energy distribution structures) are considered for MLE. First, the microdiamonds containing nitrogen‐vacancy (NV) color centers are embedded into polydimethylsiloxane (PDMS) to fabricate PUFs. Using an optical imaging system, four codable images of four noncorrelated structures are read. The noncorrelation of the four‐level encoding structure is verified by calculating the Hamming distance (0.496 ± 0.02). The results show that EC exponentially improves to 24×10 000/(100 pixels)2. Furthermore, the encoding method based on the energy level does not depend on physical structure parameters, such as the size and position of the spin structure. Thus, it is protected from structural modeling attacks, resulting in high security. Moreover, PUF labels based on PDMS flexible substrates can be employed for various flexible applications. In the proposed scheme, the information is encrypted by a four‐level two‐dimensional (2D) barcode and decoded by self‐developed PUF authentication software. The proposed scheme presents a way for developing next‐generation PUFs with super‐high EC. [ABSTRACT FROM AUTHOR]

Nowadays, product development is challenged by increasing system complexity and stringent time-to-market. To handle the demanding market requirements, knowledge from prior product generations is used to derive new, but partially similar product versions. The concept of product generation engineering, hence, allows manufacturers to release high-quality products within short development times. Therefore, in this paper, we propose a novel approach to evaluate the similarity of two product implementations based on the concept of the Hamming distance. This allows the usage of similarity information in various heuristics as well as in strategies and thus, to improve the product design process. In a wide set of cases, we investigate the quality and similarity of design points. In the experiments, the use of strategies leads to significantly short searching times, but also tends to be too restrictive in certain cases. Simultaneously, the quality of the solutions found in the heuristic design space exploration has been shown to be as good or better than for the search from scratch and considerably closer solutions as part of the non-dominated solution front have been found. [ABSTRACT FROM AUTHOR]

Shape recognition is an active research topic in the field of computer vision and graphic computing. Nevertheless, existing methods are still poor in accuracy and efficiency in some extent, which greatly limits their application in computer vision system. This paper investigates the restraint of feature structure that intrinsically deteriorates recognition performance. Furthermore, we propose a fast shape recognition method based on a bi-level restraint reduction of contour coding (CC2RR), which provides more effective theoretical support for the practical application of the visual algorithm. CC2RR reduces restraint performed from contour feature extraction and expression, respectively. First, for shape contour, the restraint of contour feature extraction is reduced by transforming the direction of contour points to contour segments; second, for the encoded contour segment, the restraint of the contour feature expression is reduced; in other words, the current direction is reduced to the previous and the next direction. Guided by these insights, Hamming code distance is used to match the coding features after the twofold restraint reduction, and the results are obtained. Experimental results verify that the method significantly improves the performance, which runs up to 500 times faster than the existing description methods based on shape contours while increasing robustness. This makes the method useful in practical software system. [ABSTRACT FROM AUTHOR]

A similar waveform search plays a crucial role in seismology for detecting seismic events, such as small earthquakes and low-frequency events. However, the high computational costs associated with waveform cross-correlation calculations represent bottlenecks during the analysis of long, continuous records obtained from numerous stations. In this study, we developed a deep-learning network to obtain 64-bit hash codes containing information on seismic waveforms. Using this network, we performed a similar waveform search for ∼35 million moving windows developed for the 30 min waveforms recorded continuously at 10 MHz sampling rates using 16 acoustic emission transducers during a laboratory hydraulic fracturing experiment. The sampling points of each channel corresponded to those of the 5.8-yr records obtained from typical seismic observations at 100 Hz sampling rates. Of the 35 million windows, we searched for windows with small average Hamming distances among the hash codes of 16 channel waveforms against template hash codes of 6057 events that were catalogued using conventional autoprocessing techniques. The calculation of average Hamming distances is 1430–1530 times faster than that of the corresponding network correlation. This hashing-based template matching enabled the detection of 23   462 additional events. We also demonstrated the feasibility of the hashing-based autocorrelation analysis, where similar event pairs were extracted without templates, by calculating the average Hamming distances for all possible pairs of the ∼35 million windows. This calculation required only 15.5 h under 120 thread parallelization. This deep hashing approach significantly reduced the required memory compared with locality-sensitive hashing approaches based on random permutations, enabling similar waveform searching on a large-scale data set. [ABSTRACT FROM AUTHOR]

Existing biohashing authentication schemes usually store bioinformation in the cloud, whose storage method can face the problems of bioinformation leakage and tampering, and single point of failure. To address the above problems, a ciphertext speech biohashing authentication scheme based on chaotic system and improved public chain is proposed. First, the fusion features are extracted and generated with the inner product of 3D chaotic system to generate the biohashing sequence. Second, the original speech is encrypted and uploaded to the InterPlanetary file system and then, uploaded to the blockchain after the data are encrypted. Finally, the server matches the hash sequence queried on the chain with the to-be-authenticated hash at the user's end using the Hamming distance. The experimental results show that the biohashing algorithm in this paper has better distinguishability, robustness, security and time efficiency. The key space of the speech encryption algorithm is 2 256 , which can well resist the exhaustive attack and ensure the security during speech authentication. In this paper, the combination of on-chain and off-chain storage of speech information can prevent the problem of bioinformation leakage and tampering, as well as avoiding the problem of single point of failure. [ABSTRACT FROM AUTHOR]

Testing modern integrated circuit (IC) is challenging with introduction of System-on Chip (SOC). Enormous test pattern count is required to authenticate the IC which inflates power consumption. Henceforth, it is mandatory to incorporate power minimization techniques at circuit design phase. Optimizing test patterns is one such effective technique that curtails test power without altering circuit design. In this paper, a new test pattern reordering algorithm is proposed based on Recurrent Neural Network. Hopfield Neural Network (HNN) constitutes an optimized solution for solving traveling salesman problem (TSP). Since, test pattern reordering can be interpreted as TSP, optimized test pattern order is attained with HNN. Energy function of this algorithm falls into local minimum, so to eradicate this issue, the algorithm is modified. Test patterns are reordered with minimal hamming distance among consecutive test patterns. The proposed algorithm is implemented in ISCAS'89 Sequential Benchmark circuits. Experimental results prove that significant reduction in transition count is accomplished with proposed algorithm without compromising fault coverage. [ABSTRACT FROM AUTHOR]

We developed a macroscopic description of the evolutionary dynamics by following the temporal dynamics of the total Shannon entropy of sequences, denoted by S, and the average Hamming distance between them, denoted by H. We argue that a biological system can persist in the so-called quasi-equilibrium state for an extended period, characterized by strong correlations between S and H, before undergoing a phase transition to another quasi-equilibrium state. To demonstrate the results, we conducted a statistical analysis of SARS-CoV-2 data from the United Kingdom during the period between March 2020 and December 2023. From a purely theoretical perspective, this allowed us to systematically study various types of phase transitions described by a discontinuous change in the thermodynamic parameters. From a more-practical point of view, the analysis can be used, for example, as an early warning system for pandemics. [ABSTRACT FROM AUTHOR]

Cognitive diagnosis models provide diagnostic information on whether examinees have mastered the skills, called "attributes," that characterize a given knowledge domain. Based on attribute mastery, distinct proficiency classes are defined to which examinees are assigned based on their item responses. Attributes are typically perceived as binary. However, polytomous attributes may yield higher precision in the assessment of examinees' attribute mastery. Karelitz (2004) introduced the ordered-category attribute coding framework (OCAC) to accommodate polytomous attributes. Other approaches to handle polytomous attributes in cognitive diagnosis have been proposed in the literature. However, the heavy parameterization of these models often created difficulties in fitting these models. In this article, a nonparametric method for cognitive diagnosis is proposed for use with polytomous attributes, called the nonparametric polytomous attributes diagnostic classification (NPADC) method, that relies on an adaptation of the OCAC framework. The new NPADC method proposed here can be used with various cognitive diagnosis models. It does not require large sample sizes; it is computationally efficient and highly effective as is evidenced by the recovery rates of the proficiency classes observed in large-scale simulation studies. The NPADC method is also used with a real-world data set. [ABSTRACT FROM AUTHOR]

Since the reform and opening up, China's foreign trade has developed rapidly. The import and export trade volume has doubled, and the team of foreign trade enterprises is constantly expanding. After China's accession to the WTO and the liberalization of foreign trade management rights, a group of small and medium-sized foreign trade enterprises have grown up in recent years and are becoming a new force in the development of China's foreign trade industry. So, how to make these enterprises grow and grow in the international market competition, and focus on cultivating the core competitiveness of small and medium-sized foreign trade enterprises, has become a strategic issue related to the overall development of foreign trade. Recently, COmbinative Distance-based ASsessment (CODAS) method computes the Euclidean distances as the primary measure and Hamming distances as the secondary measure to assess alternatives based on predetermined attribute. Moreover, the probabilistic linguistic term sets (PLTSs) are effective tool for depicting uncertainty of the multiple attribute group decision making (MAGDM) problems. The core competitiveness evaluation of small and medium-sized foreign trade enterprises is a classical MAGDM. In this paper, the CODAS method is extended to the MAGDM with PLTSs. Firstly, the definition of PLSs is introduced. Then, on the basis of the classical CODAS method, the PL-CODAS method is proposed to cope with MAGDM under PLTSs and its significant characteristic is that it can fully consider PLED and PLHD. Finally, a practical example for core competitiveness evaluation of small and medium-sized foreign trade enterprises is given to verify the developed approach and some comparative analysis was also given to verify the PL-CODAS approach. [ABSTRACT FROM AUTHOR]

As available genomic interval data increase in scale, we require fast systems to search them. A common approach is simple string matching to compare a search term to metadata, but this is limited by incomplete or inaccurate annotations. An alternative is to compare data directly through genomic region overlap analysis, but this approach leads to challenges like sparsity, high dimensionality, and computational expense. We require novel methods to quickly and flexibly query large, messy genomic interval databases. Here, we develop a genomic interval search system using representation learning. We train numerical embeddings for a collection of region sets simultaneously with their metadata labels, capturing similarity between region sets and their metadata in a low-dimensional space. Using these learned co-embeddings, we develop a system that solves three related information retrieval tasks using embedding distance computations: retrieving region sets related to a user query string, suggesting new labels for database region sets, and retrieving database region sets similar to a query region set. We evaluate these use cases and show that jointly learned representations of region sets and metadata are a promising approach for fast, flexible, and accurate genomic region information retrieval. [ABSTRACT FROM AUTHOR]

We introduce a variant of PCPs, that we refer to as rectangular PCPs, wherein proofs are thought of as square matrices, and the random coins used by the verifier can be partitioned into two disjoint sets, one determining the row of each query and the other determining the column. We construct PCPs that are efficient, short, smooth and (almost-)rectangular. As a key application, we show that proofs for hard languages in NTIME(2n), when viewed as matrices, are rigid infinitely often. This strengthens and simplifies a recent result of Alman and Chen [FOCS, 2019] constructing explicit rigid matrices in FNP. Namely, we prove the following theorem: - There is a constant δ∈(0,1) such that there is an FNP-machine that, for infinitely many N, on input 1N outputs NXN matrices with entries in F2 that are δN²-far (in Hamming distance) from matrices of rank at most 2logN/Ω(loglogN). Our construction of rectangular PCPs starts with an analysis of how randomness yields queries in the Reed--Muller-based outer PCP of Ben-Sasson, Goldreich, Harsha, Sudan and Vadhan [SICOMP, 2006; CCC, 2005]. We then show how to preserve rectangularity under PCP composition and a smoothness-inducing transformation. This warrants refined and stronger notions of rectangularity, which we prove for the outer PCP and its transforms. [ABSTRACT FROM AUTHOR]

In this work, we demonstrate the coexistence of nonvolatile memory (NVM) and volatile threshold switching (VTS) behaviors in an Ag-embedded sodium-alginate-based memristor using the current pulse mode. High and low compliance currents allow the device to present stable and reliable NVM and VTS behaviors, respectively. Specifically, NVM and VTS behaviors randomly occur under a compliance current of 40 μA. On this basis, four polygon Boolean operations (AND, OR, NOT, and XOR) and physical unclonable functions (PUFs) with an inter-class Hamming distance of 50.75% are demonstrated simultaneously in memristive devices. Adopting PUF keys, image encryption and decryption are implemented by executing the XOR logic operation. Our memristive devices have the ability of in-memory computing and providing PUFs simultaneously and thus great potential for hardware security applications. [ABSTRACT FROM AUTHOR]

Aiming at the uncertainty in the process of landslide hazard evaluation along the railway, game theory and cloud model evaluation methods were introduced, and Hamming distance was used to verify the similarity comparison results to evaluate the landslide hazard along the railway. Firstly, 12 influencing factors such as rainfall, stratigraphic lithology and land use type were selected to establish the risk evaluation index system. Secondly, the improved CRITIC method, coefficient of variation method, G1 method and order diagram method were adopted to determine the basic weight. Then the game theory is introduced to calculate the optimal weight, and the comprehensive evaluation cloud is determined according to the cloud model and the optimal weight of each evaluation index. Finally, the Hamming distance is used to verify the similarity comparison results of the comprehensive evaluation cloud and the evaluation standard cloud, and the landslide risk grade is determined according to the maximum similarity principle, and the landslide risk distribution map of the study area is generated based on ArcGIS. The results show that the landslide hazard grade is level four, which is basically consistent with the actual situation. The landslide hazard evaluation model based on game theory and cloud model can accurately reflect the grade of landslide. The cloud model is used to realize the conversion of qualitative and quantitative problems, which makes the landslide hazard level more intuitive and has certain reference value for landslide hazard risk management. [ABSTRACT FROM AUTHOR]

Structure learning is the core of graph model Bayesian Network learning, and the current mainstream single search algorithm has problems such as poor learning effect, fuzzy initial network, and easy falling into local optimum. In this paper, we propose a heuristic learning algorithm HC-PSO combining the HC (Hill Climbing) algorithm and PSO (Particle Swarm Optimization) algorithm, which firstly uses HC algorithm to search for locally optimal network structures, takes these networks as the initial networks, then introduces mutation operator and crossover operator, and uses PSO algorithm for global search. Meanwhile, we use the DE (Differential Evolution) strategy to select the mutation operator and crossover operator. Finally, experiments are conducted in four different datasets to calculate BIC (Bayesian Information Criterion) and HD (Hamming Distance), and comparative analysis is made with other algorithms, the structure shows that the HC-PSO algorithm is superior in feasibility and accuracy. [ABSTRACT FROM AUTHOR]

Currently, the digital economy continues to deepen its development, and it has become a consensus among all sectors as the direction of global future development. Digital finance, as a fleet in the wave of digital economy, is rapidly heading towards the sunny shore of benefiting the public and serving entities driven by the digital technology engine. Xinwang Bank is a fast boat in the digital finance fleet, always adhering to the principle of technology, building an open platform, and actively promoting the construction of an open, shared, and secure digital credit ecosystem from three levels: institutional, industry, and ecological, to assist in the development of the digital economy. The digital commercial bank security evaluation is a classical multiple attribute group decision making (MAGDM) problems. Recently, the Evaluation based on Distance from Average Solution (EDAS) method has been employed to manage MAGDM issues. The intuitionistic fuzzy sets (IFSs) are used as a tool for portraying uncertain information during the digital commercial bank security evaluation. In this paper, the intuitionistic fuzzy nunmber EDAS (IFN-EDAS) method is cultivated to manage the MAGDM based on Hamming distance and Euclidean distance under IFSs. In the end, a numerical case study for digital commercial bank security evaluation is supplied to validate the proposed method. The main contributions of this paper are outlined: (1) the EDAS method has been extended to IFSs based on Hamming distance and Euclidean distance; (2) the CRITIC method is used to derive weight based on Hamming distance and Euclidean distance under IFSs. (3) the IFN-EDAS method based on Hamming distance and Euclidean distance is founded to manage the MAGDM based on the Hamming distance and Euclidean distance under IFSs; (4) a numerical case study for digital commercial bank security evaluation and some comparative analysis is supplied to validate the proposed method. [ABSTRACT FROM AUTHOR]

In the information age, teachers are no longer the only source of information for students, and the problems of the traditional lecture mode are becoming more and more obvious. Especially in the process of teaching English in colleges and universities, students' personalized and diversified needs for English learning are becoming more and more obvious, and if traditional theoretical lectures or theoretical indoctrination are continued, students' needs and goals will be affected. As a new form of teaching, blended teaching can integrate the advantages of classroom teaching and online teaching and improve the overall quality and effectiveness of English teaching under the premise of changing the orientation of teachers and students. The English blended teaching quality evaluation is a multiple-attribute group decision-making (MAGDM) problem involving multiple qualitative and quantitative attributes. In this paper, on basis of EDAS technique, a novel spherical fuzzy number EDAS (SFN-EDAS) technique based on SFN Hamming distance (SFNHD) and SFN Jaccard similarity measure (SFNJSM) is built for dealing with MAGDM. Moreover, the MEREC technique with SFNHD and SFNJSM is extended to SFSs to acquire the attribute weights. Finally, SFN-EDAS technique is used for English blended teaching quality evaluation to prove practicability of the developed SFN-EDAS technique and SFN-EDAS technique is compared with existing techniques to further demonstrate its superiority. Hence, the research main achievements for this paper are outlined: (1) the novel EDAS technique is extended to the SFSs environment based on the SFNHD and SFNJSM; (2) the MEREC technique is extended to SFSs to acquire the attribute weights based on the SFNHD and SFNJSM; (3) a novel spherical fuzzy number EDAS (SFN-EDAS) technique based on the SFNHD and SFNJSM is built for dealing with MAGDM; (4) an empirical example and several comparative analysis for English blended teaching quality evaluation is offered to demonstrate the SFN-EDAS technique. [ABSTRACT FROM AUTHOR]

The content-based speech retrieval scheme using the hash technique boasts high accuracy; however, its index structures, such as the hash table, suffer from high space cost, low search efficiency, and security concerns. Moreover, implementing keyword search or ranking retrieval is a challenging task. We suggest a secure content-based retrieval scheme that utilizes a novel hashing-inverted index to address these issues. This method converts hash sequences into keyword sequences by employing a keyword hash template to establish a hashing-inverted index. The index table and all keywords are encrypted using a lightweight homomorphic encryption scheme to ensure security. When the data user submits a trapdoor of a spoken query, the cloud server performs homomorphic Hamming distance calculation and homomorphic location calculation, which makes the search process much more secure. The experimental results show that our index is lighter, easy to maintain, and expand. The retrieval efficiency is also higher. Security analysis proves that the privacy of the index and speeches is preserved. Generally, our scheme is accurate, efficient, and secure for the massive spoken audio data. [ABSTRACT FROM AUTHOR]

In this paper, we design a quantum associative classifier, which includes pattern storage and pattern classification. For pattern storage, we present a method to construct quantum superposition states with arbitrary number of basis states. The method employs three groups of qubits to encode the sequence number, category, and value of each sample respectively. Firstly, according to the total number of samples N to be stored, the number of sequence number qubits needed by associative classifier is calculated. Then, some quantum rotation gates and Hadamard gates are used to transform the sequence number qubits with initial state of | 0 ⟩ into the quantum equilibrium superposition state which contains exactly N basis states. Finally, according to the category and value of the sample to be stored, the remaining two groups of qubits with initial state of | 0 ⟩ are converted into corresponding quantum basis states by some controlled operations. For associative classification, we design a classification method based on quantum minimum search. Firstly, the Hamming distances between the input sample and all the stored samples are calculated, and then Grover’s algorithm with fixed phase is used to search the minimum value of these Hamming distances. At this time, the category of the stored sample corresponding to the minimum value is the category of the input sample. The specific classification results can be obtained through the measurement of quantum state in the register. The number of iterations of the search is exponentially lower than that of its classical counterpart, so that pattern classification can be realized quickly. [ABSTRACT FROM AUTHOR]

Feature extraction and matching are the basic procedures of the so-called Visual Odometer (VO), Simultaneous Localization and Mapping (SLAM) and many other image processing algorithms. Oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary features (ORB) algorithm are widely used since they are computationally faster. In this paper, we proposed a method to generate a value for a feature, the value is called signature. In the matching step, we only compute Hamming distances of descriptors with the same signatures. Hence, the matching time is shortened. Compared with the original ORB algorithm, features to be matched dropped 69.63% on TUM datasets and 85.7% on VGG datasets by adopting our strategy. In addition, the precision is above 85% on both VGG and TUM datasets. We design a customized hardware architecture for ORB feature extraction and matching based on the proposed method. The hardware structure is implemented on Xilinx ZCU102 evaluation board. The clock frequency is set to 150 MHz. Our Field Programmable Gate Arrays (FPGA) system achieves 193 fps on 1 2 8 0 × 7 2 0 images with 1984 features on average and 314 fps on 6 4 0 × 4 8 0 images with 700 features on average, which is more efficient compared to the state-of-the-art works. [ABSTRACT FROM AUTHOR]

In the Antibody Mediated Prevention (AMP) trials (HVTN 704/HPTN 085 and HVTN 703/HPTN 081), prevention efficacy (PE) of the monoclonal broadly neutralizing antibody (bnAb) VRC01 (vs. placebo) against HIV-1 acquisition diagnosis varied according to the HIV-1 Envelope (Env) neutralization sensitivity to VRC01, as measured by 80% inhibitory concentration (IC80). Here, we performed a genotypic sieve analysis, a complementary approach to gaining insight into correlates of protection that assesses how PE varies with HIV-1 sequence features. We analyzed HIV-1 Env amino acid (AA) sequences from the earliest available HIV-1 RNA-positive plasma samples from AMP participants diagnosed with HIV-1 and identified Env sequence features that associated with PE. The strongest Env AA sequence correlate in both trials was VRC01 epitope distance that quantifies the divergence of the VRC01 epitope in an acquired HIV-1 isolate from the VRC01 epitope of reference HIV-1 strains that were most sensitive to VRC01-mediated neutralization. In HVTN 704/HPTN 085, the Env sequence-based predicted probability that VRC01 IC80 against the acquired isolate exceeded 1 µg/mL also significantly associated with PE. In HVTN 703/HPTN 081, a physicochemical-weighted Hamming distance across 50 VRC01 binding-associated Env AA positions of the acquired isolate from the most VRC01-sensitive HIV-1 strain significantly associated with PE. These results suggest that incorporating mutation scoring by BLOSUM62 and weighting by the strength of interactions at AA positions in the epitope:VRC01 interface can optimize performance of an Env sequence-based biomarker of VRC01 prevention efficacy. Future work could determine whether these results extend to other bnAbs and bnAb combinations. [ABSTRACT FROM AUTHOR]

The incorporation of information and communication technologies in the power grids has greatly enhanced efficiency in the management of demand-responses. In addition, smart grids have seen considerable minimization in energy consumption and enhancement in power supply quality. However, the transmission of control and consumption information over open public communication channels renders the transmitted messages vulnerable to numerous security and privacy violations. Although many authentication and key agreement protocols have been developed to counter these issues, the achievement of ideal security and privacy levels at optimal performance still remains an uphill task. In this paper, we leverage on Hamming distance, elliptic curve cryptography, smart cards and biometrics to develop an authentication protocol. It is formally analyzed using the Burrows-Abadi-Needham (BAN) logic, which shows strong mutual authentication and session key negotiation. Its semantic security analysis demonstrates its robustness under all the assumptions of the Dolev-Yao (DY) and Canetti- Krawczyk (CK) threat models. From the performance perspective, it is shown to incur communication, storage and computation complexities compared with other related state of the art protocols. [ABSTRACT FROM AUTHOR]

Recent controllability analyses have demonstrated that driver nodes tend to be associated to genes related to important biological functions as well as human diseases. While researchers have focused on identifying critical nodes, intermittent nodes have received much less attention. Here, we propose a new efficient algorithm based on the Hamming distance for computing the importance of intermittent nodes using a Minimum Dominating Set (MDS)-based control model. We refer to this metric as criticality. The application of the proposed algorithm to compute criticality under the MDS control framework allows us to unveil the biological importance and roles of the intermittent nodes in different network systems, from cellular level such as signaling pathways and cell-cell interactions such as cytokine networks, to the complete nervous system of the nematode worm C. elegans. Taken together, the developed computational tools may open new avenues for investigating the role of intermittent nodes in many biological systems of interest in the context of network control. [ABSTRACT FROM AUTHOR]

The rapid progress in the transfer of information and its availability are the reasons behind the widespread use of the Internet. Document images are the most complicated and challenging category among various forms of digital data to which watermarking can be applied for its security and authentication. Watermarking of the document image is a very difficult task, since these images have very limited redundancy. Because of this factor there has been very little research in this area. A comprehensive research should be carried out to ensure the effective assessment, review as well as implementation of document image watermarking techniques. This paper reviews the existing document image watermarking by considering different evaluation parameters. Based on this review, a variety of challenges and directions have been prescribed for the development of effective watermarking techniques for document images. [ABSTRACT FROM AUTHOR]

Quantum approximate optimization algorithm (QAOA) is a promising hybrid quantum-classical algorithm to solve combinatorial optimization problems in the era of noisy intermediate-scale quantum computers. Recently it has been revealed that warm-start approaches can improve the performance of QAOA, where approximate solutions are obtained by classical algorithms in advance and incorporated into the initial state and/or unitary ansatz. In this work, we study in detail how the accuracy of approximate solutions affects the performance of the warm-start QAOA (WS-QAOA). We numerically find that in typical MAX-CUT problems, WS-QAOA achieves higher fidelity (probability that exact solutions are observed) and approximation ratio than QAOA as the Hamming distance of approximate solutions to the exact ones becomes smaller. We reveal that this could be quantitatively attributed to the initial state of the ansatz. We also solve MAX-CUT problems by WS-QAOA with approximate solutions obtained via QAOA, having higher fidelity and approximation ratio than QAOA especially when the circuit is relatively shallow. We believe that our study may deepen understanding of the performance of WS-QAOA and also provide a guide as to the necessary quality of approximate solutions. [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]

Sports events, as large-scale events that provide products and services, have received widespread attention for their economic benefits and influence. Event organizers expect to achieve high efficiency by providing high-quality products and services. The quality of competition products and services is mainly evaluated through the subjective feelings of the audience, and usually the audience's evaluation of service quality is vague. Therefore, this article intends to establish an evaluation index system for the quality of spectator service in sports events, in order to provide a reasonable evaluation of the service products provided by sports event organizers. The audience service quality evaluation for large-scale sports-events is a MAGDM problems. Recently, the EDAS and CRITIC technique has been employed to cope with MAGDM issues. The interval neutrosophic sets (INSs) are employed as a tool for characterizing uncertain information during the audience service quality evaluation for large-scale sports-events. In this paper, the interval neutrosophic number EDAS (INN-EDAS) technique based on the Hamming distance and Euclid distance is founded to manage the MAGDM under INSs. The CRITIC technique is employed to obtain the weight information based on the Hamming distance and Euclid distance under INSs. Finally, a numerical case study for audience service quality evaluation for large-scale sports-events is employed to validate the proposed technique. The main contributions of this paper are proposed: (1) The INN-EDAS technique based on the Hamming distance and Euclid distance is founded to manage the MAGDM under INSs; (2) The CRITIC technique is employed to obtain the weight information based on the Hamming distance and Euclid distance under INSs; (3) a numerical case study for audience service quality evaluation for large-scale sports-events is employed to validate the proposed technique. [ABSTRACT FROM AUTHOR]

As the rapidly progressing applications of uncertainty theories, the need for modifications to some of their existing mathematical tools or creating new tools to deal correctly with them in various environments is also exposed. Hesitant fuzzy numbers (HFNs), as a particular case of fuzzy numbers, are not an exception to this rule. Considering the necessity of determining the distance between given HFNs in many of their practical applications, this article shows that the existing methods either do not provide correct results or are not able to meet the needs of users. This paper aims to present new methods for distance measures of hesitant fuzzy numbers. To do them, three prevalent distance measures, i.e., the generalized distance measure, the Hamming distance measure, and the Euclidean distance measure, will be optimized into three distinct trinal categories. With the approach of reducing error propagation via reducing some unnecessary mathematical computations, new distance measures on HFNs will be introduced, first. The middle is the modification of the first category, which is more suitable when the given HFNs are equal-distance by the previous formula. Also, as the third category, the weighted form of these distance measures has been proposed, to be used where the real and membership parts of HFNs are not of equal importance. As an application of these, a TOPSIS-based technique for solving multi-attribute group decision-making problems with HFNs has been proposed. A numerical example will be implemented to describe the presented method. Finally, along with the validation of the proposed method, its numerical comparison with some other existing methods will be discussed in detail. [ABSTRACT FROM AUTHOR]

The financial management capability of enterprises, as an important component of their soft power, has a decisive impact on the success or failure of their operations. In the increasingly fierce market competition, enterprises must continuously improve their financial management capabilities in order to ensure efficient operation and achieve better economic benefits. Insufficient financial management capabilities in enterprises can seriously affect the stability of production and operation, hinder the realization of profits, and hinder the long-term development of enterprises. In order to better improve the financial management level of enterprises and promote the standardization of financial management, it is necessary to use scientific techniques to evaluate the financial management ability of enterprises, so as to accurately grasp the key links in the financial management process of enterprises and implement targeted effective measures. The enterprise financial management capability evaluation is a classical multiple attribute group decision making (MAGDM). In recent years, the MAGDM problem has become an important research field in modern decision science. This paper extends the EDAS technique to the 2-tuple linguistic Pythagorean fuzzy sets (2TLPFSs). On the basis of the original EDAS technique, 2-tuple linguistic Pythagorean fuzzy number EDAS (2TLPFN-EDAS) technique based on cosine similarity measure (CSM) and Hamming distances is managed for MAGDM. Finally, a case study for enterprise financial management capability evaluation and some comparative analysis with the other techniques show that the new technique proposed in this paper is effective, reasonable and accurate. The main contribution of the paper is summarized: (1) the 2TLPFN-EDAS technique based on CSM and Hamming distances is managed for MAGDM under 2TLPFSs; (2) The entropy is employed to manage the attribute weight based on cosine similarity measure(CSM) and Hamming distances under 2TLPFSs; (3) the 2TLPFN-EDAS technique is employed for enterprise financial management capability evaluation and were compared with some existing techniques; (4) Through the comparison, it is found that 2TLPFN-EDAS technique for enterprise financial management capability evaluation proposed are effective. [ABSTRACT FROM AUTHOR]

Sequence motif discovery algorithms enhance the identification of novel deoxyribonucleic acid sequences with pivotal biological significance, especially transcription factor (TF)-binding motifs. The advent of assay for transposase-accessible chromatin using sequencing (ATAC-seq) has broadened the toolkit for motif characterization. Nonetheless, prevailing computational approaches have focused on delineating TF-binding footprints, with motif discovery receiving less attention. Herein, we present Cis rEgulatory Motif Influence using de Bruijn Graph (CEMIG), an algorithm leveraging de Bruijn and Hamming distance graph paradigms to predict and map motif sites. Assessment on 129 ATAC-seq datasets from the Cistrome Data Browser demonstrates CEMIG's exceptional performance, surpassing three established methodologies on four evaluative metrics. CEMIG accurately identifies both cell-type-specific and common TF motifs within GM12878 and K562 cell lines, demonstrating its comparative genomic capabilities in the identification of evolutionary conservation and cell-type specificity. In-depth transcriptional and functional genomic studies have validated the functional relevance of CEMIG-identified motifs across various cell types. CEMIG is available at https://github.com/OSU-BMBL/CEMIG , developed in C++ to ensure cross-platform compatibility with Linux, macOS and Windows operating systems. [ABSTRACT FROM AUTHOR]

After the tremendous development and fast growth in communication systems and electronic devices, information security and preservation have become one of the most important procedures to be considered when transferring data between two parties. Encryption and data hiding are two of the most important techniques to protect the information sent over networks and the Internet. In this paper, a new technique of encryption and concealment is used through the following steps: First, the secret information is encrypted by using developed DNA to get (S1), second, by scrambling the encrypted data, the (S1) is diffused by using a zigzag method to get (S2), third, re-obfuscation the (S2) by using developed zigzag method through using Tinkerbell technique to get (S3), fourth, perform XoR operation between the (S3) and the Tinkerbell technique to get (S4), and fifth, the (S4) is hidden in the random locations of the cover image based on the LSB technique and the Tinkerbell technology, where the Tinkerbell has incredibly excellent characteristics and used to generate random sequence numbers twice with different initial and parameters value, once for the encryption process for scrambling secret information and once for the hiding process for determining hidden locations. Several metrics were used to evaluate the system's efficiency: histogram, PSNR, MSE, mean absolute error, bit error rate, encryption quality, Unified Average Changing Intensity, hamming distance, entropy, and correlation coefficient. The experimental results display that the suggested method has a good performance and reliability, high security, and greater embedding capacity and ensures higher image quality; also, the proposed method presented preferable outcomes when compared to other approaches. [ABSTRACT FROM AUTHOR]

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]

While the theoretical analysis of evolutionary algorithms (EAs) has made significant progress for pseudo-Boolean optimization problems in the last 25 years, only sporadic theoretical results exist on how EAs solve permutation-based problems. To overcome the lack of permutation-based benchmark problems, we propose a general way to transfer the classic pseudo-Boolean benchmarks into benchmarks defined on sets of permutations. We then conduct a rigorous runtime analysis of the permutation-based (1 + 1) EA proposed by Scharnow et al. (J Math Model Algorithm 3:349–366, 2004) on the analogues of the LeadingOnes and Jump benchmarks. The latter shows that, different from bit-strings, it is not only the Hamming distance that determines how difficult it is to mutate a permutation σ into another one τ , but also the precise cycle structure of σ τ - 1 . For this reason, we also regard the more symmetric scramble mutation operator. We observe that it not only leads to simpler proofs, but also reduces the runtime on jump functions with odd jump size by a factor of Θ (n) . Finally, we show that a heavy-tailed version of the scramble operator, as in the bit-string case, leads to a speed-up of order m Θ (m) on jump functions with jump size m. A short empirical analysis confirms these findings, but also reveals that small implementation details like the rate of void mutations can make an important difference. [ABSTRACT FROM AUTHOR]

Geospatial data conflation is the process of identifying and merging the corresponding features in two datasets that represent the same objects in reality. Conflation is needed in a wide range of geospatial analyses, yet it is a difficult task, often considered too unreliable and costly due to various discrepancies between GIS data sources. This study addresses the reliability issue of computerized conflation by developing stronger optimization-based conflation models for matching two network datasets with minimum discrepancy. Conventional models match roads on a feature-by-feature basis. By comparison, we propose a new node-arc conflation model that simultaneously matches road-center lines and junctions in a topologically consistent manner. Enforcing this topological consistency increases the reliability of conflation and reduces false matches. Similar to the well-known rubber-sheeting method, our model allows for the use of network junctions as "control" points for matching network edges. Unlike rubber sheeting, the new model is automatic and matches all junctions (and edges) in one pass. To the best of our knowledge, this is the first optimized conflation model that can match nodes and edges in one model. Computational experiments using six road networks in Santa Barbara, CA, showed that the new model is selective and reduces false matches more than existing optimized conflation models. On average, it achieves a precision of 94.7% with over 81% recall and achieves a 99.4% precision when enhanced with string distances. [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]

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]

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]

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]