Hamming Distance Research Articles

Three-dimensional constellation design based on centroid-corrected geometric shaping with optimized average Hamming distance

In this paper, a novel (to our knowledge) three-dimensional (3D) constellation design based on centroid-corrected geometric shaping (CCGS) with optimized average Hamming distance (OAHD) is proposed, referred to as 3D-CCGS-OAHD. This scheme employs a centroid-correction technique to optimize the constellation point distribution and ensure a balanced geometry for higher constellation figure of merit (CFM). Meanwhile, a simulated annealing (SA) algorithm is used to minimize the average Hamming distance (AHD) between adjacent constellation points and explore a better bit-to-symbol mapping strategy superior to Gray mapping to improve the bit error rate (BER) performance. The 3D-CCGS-OAHD scheme was successfully demonstrated over a 2 km 7-core fiber. Results show that the 3D-CCGS constellation achieves a sensitivity gain of 0.84 dB compared to the traditional 3D constellation under Gray mapping. In addition, the 3D-CCGS-OAHD scheme achieves a gain of 1.29 dB compared to the traditional 3D constellation. The experiment results demonstrate that the proposed scheme is suitable for short-distance optical communication systems and has promising research prospects.

Advanced CoCoSo Technique for Interval Neutrosophic MAGDM and Applications to Quality Evaluation in Higher Education Management

The evaluation of quality in higher education management focuses on assessing the effectiveness, efficiency, and overall performance of administrative processes within higher education institutions. This evaluation also identifies areas for improvement, fosters accountability, and supports continuous development to enhance institutional performance and competitiveness in a rapidly changing educational environment. The quality evaluation in higher education management is a Multiple-Attribute Group Decision-Making (MAGDM) problem. In this study, the CoCoSo approach is identified for MAGDM with INSs Interval neutrosophic sets (INSs). Subsequently, the interval neutrosophic number Hausdorff Hamming distance CoCoSo (INN-HHD-CoCoSo) approach in light with interval neutrosophic number Hausdorff Hamming distance (INNHHD), is developed for MAGDM. The entropy approach is utilized to determine weight in light with INNHHD under INNs. Ultimately, numerical example for the quality evaluation in higher education management is provided to illustrate the INN-HHD-CoCoSo approach.

Fault diagnosis method of Bayesian network based on association rules

When the number of samples is large, the scale of the Bayesian network (BN) structure search space increases exponentially with the number of nodes, resulting in a sharp increase in the difficulty of learning the BN structure. Aiming at this problem, this paper proposes a fault diagnosis model construction method combining association rules and a BN network. The Euclidean distance under the Symbolic Aggregation Approximation (SAX) algorithm is utilized to compute and average the distance between the standard and faulty samples and filter the candidate nodes by the average value, which in turn reduces the search sample space. The method of combining Association Rules algorithm with traditional BN structure learning results is used to solve the problem of wrong edges in structure learning. Finally, the maximum likelihood estimation method is used for parameter learning to complete the construction of the diagnostic network. The experimental results show that the running time of the Bayesian Network based on the Association Rules (AR-BN) model proposed in this paper is short and that the Hamming distance with the original structure is small, so this model can effectively reduce the search space and solve the problem of wrong edges, and it also has a good performance in fault diagnosis.

BOPIM: Bayesian Optimization for influence maximization on temporal networks

The goal of influence maximization (IM) is to select a small set of seed nodes which maximizes the spread of influence on a network. In this work, we propose BOPIM , a Bayesian Optimization (BO) algorithm for IM on temporal networks. The IM task is well-suited for a BO solution due to its expensive and complicated objective function. There are at least two key challenges, however, that must be overcome, primarily due to the inputs coming from a cardinality-constrained, non-Euclidean, combinatorial space. The first is constructing the kernel function for the Gaussian Process regression. We propose two kernels, one based on the Hamming distance between seed sets and the other leveraging the Jaccard coefficient between node’s neighbors. The second challenge is the acquisition function. For this, we use the Expected Improvement function, suitably adjusting for noise in the observations, and optimize it using a greedy algorithm to account for the cardinality constraint. In numerical experiments on real-world networks, we prove that BOPIM outperforms competing methods and yields comparable influence spreads to a gold-standard greedy algorithm while being as much as ten times faster. In addition, we find that the Hamming kernel performs favorably compared to the Jaccard kernel in nearly all settings, a somewhat surprising result as the former does not explicitly account for the graph structure. Finally, we demonstrate two ways that the proposed method can quantify uncertainty in optimal seed sets. To our knowledge, this is the first attempt to look at uncertainty in the seed sets for IM.

Domain-Aware Semantic Alignment Hashing for Large-Scale Zero-Shot Image Retrieval

Hashing has been proven to be effective in the field of large-scale image retrieval. However, traditional hashing is stuck in performance dilemmas under zero-shot scenarios due to the concept shift problem. Although some zero-shot hashing methods exploit category attributes to facilitate knowledge transfer across domains, they usually struggle to generate domain-adaptive hash codes, making it hard to distinguish samples between unknown and known classes. With this motivation, we propose a novel approach called Domain-aware Semantic Alignment Zero-shot Hashing (DSAZH), which reveals three issues that suppress performance: semantic misalignment, biased optimization, and ambiguous Hamming distance. To address these challenges, multiple initiatives are innovatively integrated into a unified framework: First, it generates semantic-aligned hash codes through class-level and instance-level semantic alignment ; then it learns unbiased hash codes and domain-adaptive hash function through unbiased optimization equipped with asymmetric processing and class-prompting regression; finally, it distinguishes seen instances from unseen using domain-aware thresholding . Extensive experiments show that DSAZH achieves up to 15.82% MAP improvement ( e.g. , 69.71% vs. 53.89% on large-scale ImageNet with 256-bit codes) while reducing training time by two orders of magnitude ( e.g. , 3.07s vs. 202.84s), demonstrating its superior accuracy and efficiency compared to state-of-the-art ZSH methods. The source code is available at https://github.com/yxinwang/DSAZH .

Personalized experience: The relationship between customer preference prediction and emotional satisfaction in homestay inn design

The improvement of people's living standards has led to changes in consumer attitudes, which have transformed customers' expectations of the lodging experience from a single functional demand to the pursuit of a personalized and emotional all-round experience. As an emerging form of accommodation, the personalization of its design and service has become a highlight to attract customers. This study adopts a mixed-method research design, combining qualitative interviews and quantitative questionnaires to comprehensively analyze the design preferences and emotional experiences of homestay inn customers. Not only that, this paper also develops a customer preference prediction model based on support vector machines and quantifies the relationship between design elements and emotional satisfaction through statistical analysis methods. The results of the study show that key elements in homestay inn design, including room layout, decorative style, and personalized service, are significantly and positively related to customers' emotional satisfaction. The highest Hamming loss of only 0.11, together with the high percentage of model stability, verifies the accuracy and reliability of the customer preference prediction model. The application of the emotional satisfaction scale reveals specific customer preferences for design elements, providing homestay inn operators with an empirical basis for optimizing design and service.

HIERARCHICAL CLUSTERING USING REVERSIBLE BINARY CELLULAR AUTOMATA FOR HIGH-DIMENSIONAL DATA

This work proposes a hierarchical clustering algorithm for high-dimensional datasets using the cyclic space of reversible finite cellular automata. In cellular automaton (CA)-based clustering, if two objects belong to the same cycle, they are closely related and considered as part of the same cluster. However, if a high-dimensional dataset is clustered using the cycles of one CA, closely related objects may belong to different cycles. This paper identifies the relationship between objects in two different cycles based on the median of all elements in each cycle so that they can be grouped in the next stage. Further, to minimize the number of intermediate clusters which in turn reduces the computational cost, a rule selection strategy is taken to find the best rules based on information propagation and cycle structure. After encoding the dataset using frequency-based encoding such that the consecutive data elements maintain a minimum Hamming distance in encoded form, our proposed clustering algorithm iterates over three stages to finally cluster the data elements into the desired number of clusters given by user. When verified over standard benchmark datasets with various performance metrics, our algorithm is at par with the existing algorithms with quadratic time complexity.

Integration of Grab Methods with Artificial Neural Networks for Enhanced Decision-Making Systems

This study explores the architecture, development, and practical applications of artificial neural networks (ANNs), with advances in Gray Relational Analysis (GRA) techniques. ANNs are computational models designed to model biological neural systems, consisting of layers of interconnected neurons—i.e., input, hidden, and output layers—connected by synaptic weights. Operating through a connectionist approach, these networks effectively mimic the four basic functions of biological neurons: receiving input, integrating information, processing data, and generating output. Traditional ANNs, although powerful, face limitations in embedded systems due to their reliance on high-precision digital information transfer and the resulting resource demands. This has prompted the development of more efficient alternatives, such as spiking neural networks (SNNs), which use event-driven spiking signals to reduce power consumption and memory usage. The field has advanced further by incorporating theoretical models for quantum neural computing and the use of genetic algorithms employing various crossovers and mutation techniques. The versatility of ANNs is evident in a variety of applications. In healthcare, they aid in pattern recognition associated with conditions such as breast cancer and diabetes. For water resource management, ANN models predict relationships between rainfall and water levels. In financial sectors, they analyze complex economic conditions and assess credit risk for small business loans. Industrial applications include modelling complex systems in manufacturing plants, although widespread adoption in this field is limited. Complementing neural network advances, GRA methods have emerged to address multi-criteria decision-making challenges. Notable advances include the GRAS techniques have been developed to correct matrices with negative values, while fuzzy GRA methods now include interval-valued triangular fuzzy numbers and probabilistically uncertain linguistic word sets. Recent advances include innovations such as score values and normalized Hamming distances within single-valued neutrosophic fuzzy stein summary, these computational approaches offer powerful ways to solve complex, multidimensional problems, with recent studies highlighting improved performance and promising prospects for future application.

Integration of Grab Methods with Artificial Neural Networks for Enhanced Decision-Making Systems

This study explores the architecture, development, and practical applications of artificial neural networks (ANNs), with advances in Gray Relational Analysis (GRA) techniques. ANNs are computational models designed to model biological neural systems, consisting of layers of interconnected neurons—i.e., input, hidden, and output layers—connected by synaptic weights. Operating through a connectionist approach, these networks effectively mimic the four basic functions of biological neurons: receiving input, integrating information, processing data, and generating output. Traditional ANNs, although powerful, face limitations in embedded systems due to their reliance on high-precision digital information transfer and the resulting resource demands. This has prompted the development of more efficient alternatives, such as spiking neural networks (SNNs), which use event-driven spiking signals to reduce power consumption and memory usage. The field has advanced further by incorporating theoretical models for quantum neural computing and the use of genetic algorithms employing various crossovers and mutation techniques. The versatility of ANNs is evident in a variety of applications. In healthcare, they aid in pattern recognition associated with conditions such as breast cancer and diabetes. For water resource management, ANN models predict relationships between rainfall and water levels. In financial sectors, they analyze complex economic conditions and assess credit risk for small business loans. Industrial applications include modelling complex systems in manufacturing plants, although widespread adoption in this field is limited. Complementing neural network advances, GRA methods have emerged to address multi-criteria decision-making challenges. Notable advances include the GRAS techniques have been developed to correct matrices with negative values, while fuzzy GRA methods now include interval-valued triangular fuzzy numbers and probabilistically uncertain linguistic word sets. Recent advances include innovations such as score values and normalized Hamming distances within single-valued neutrosophic fuzzy stein summary, these computational approaches offer powerful ways to solve complex, multidimensional problems, with recent studies highlighting improved performance and promising prospects for future application.

Online weighted hashing for cross-modal retrieval

Online weighted hashing for cross-modal retrieval

Constructing Controlled Random Tests with a Small Number of Test Patterns

The article considers the issues of testing computing systems and their components. A class of controlled probabilistic tests with a small number of tests patterns is identified and studied. A method for constructing controlled probabilistic tests with a given Hamming distance is presented, the basis of which is one-dimensional scaling of templates representing tests of small bit depth. It is proposed to use exhaustive and pseudo-exhaustive tests as templates for obtaining controlled probabilistic tests. The properties of the generated tests and approaches to their use as an alternative to probabilistic tests are studied. The efficiency of the method for constructing controlled probabilistic tests is experimentally analyzed and confirmed for the case of testing memory devices for the presence of complex code-sensitive faults.

Nanoseed-based physically unclonable function for on-demand encryption.

A physically unclonable function (PUF) is a promising hardware-based cryptographic primitive to prevent confidential information leakage. However, conventional techniques, such as weak and strong PUFs, have limitations in overcoming the trade-off between security and storage volume. This study introduces nanoseed-based PUFs that overcome the drawbacks of conventional PUFs using optical and electrical randomness originated from nanoseeds and a unique on-demand cryptographic algorithm. Ideally mixed PbS quantum dots and Ag nanocrystals in the same medium are exploited as nanoseeds to simultaneously promote independent optical and electrical randomness. The number of secured keys that can be generated on-demand by combining the optical and electrical features in parallel using shuffling method is almost infinite (>1058741 per square millimeter). The proposed PUF achieves a near-ideal Hamming distance in uniqueness and randomness tests, validating its cryptographic efficacy. Last, storage-free and on-demand PUF with the shuffling method are demonstrated using smartphones, realizing manufacturer-/user-friendly cryptography system.

Correcting a Graph Into a Linegraph Minimizing Hamming Distance Edition Is NP-Complete and FPT by Treewidth

Since Beineke's work in 1968 on linegraphs, attention has focused on the classification of graphs as linegraphs. It is known that every graph $G$ is the linegraph of an hypergraph, and the question is to characterize that root graph. We introduce the $C_{p,q}$ classes, defined as sets of graphs where each vertex can be covered by at most $p$ cliques, and each edge belongs to at most $q$ cliques. These classes provide a comprehensive classification of linegraphs through a unified and parameterized approach. They describe previously known graph classes - such as linegraphs of simple graphs, $p$-uniform hypergraphs and $p$-uniform $1$-linear hypergraphs - while being capable of generalization. We study the complexity of determining the membership and edit distance of a graph to one of these classes. We prove the first Fixed Parameter Tractable algorithm with respect to treewidth to compute the edit distance.

Determining an approach to iris recognition depending on shooting conditions

The object of this study is the development and evaluation of image processing and analysis methods for iris recognition, which can be integrated into human-machine interaction (HMI) systems based on biometric data or other contactless interaction approaches. Enabling high accuracy and reliability of biometric iris recognition systems under variable imaging conditions remains an open scientific challenge. One of the primary difficulties is the impact of changing lighting conditions, head tilt, and partial eye openness on identification results. This study assesses the effect of preprocessing methods (Equalization Histogram, CLAHE) on iris image quality and compares the algorithmic method (Hamming Distance) with neural network models (CNN, DenseNet) based on key metrics, including accuracy, False Match Rate, False Non-Match Rate, and Equal Error Rate. Additionally, the influence of training dataset structure and neural network hyperparameters on classification performance was analyzed. The results demonstrate that the Hamming Distance method (HD = 0.35) achieves 95.5 % accuracy, making it a competitive alternative to neural networks. It was established that combining CLAHE and Equalization Histogram effectively reduces noise and enhances segmentation accuracy. Furthermore, it was determined that the DenseNet-201 neural network achieves an accuracy of 99.93 % when using an optimal dataset split (70 %:15 %:15 %). The study confirms that preprocessing techniques such as normalization and adaptive contrast enhancement significantly reduce recognition errors under varying lighting conditions. The proposed solution holds significant potential for assistive technologies for individuals with visual impairments, the automotive industry, as well as security systems

ConceptSearch: Towards Efficient Program Search Using LLMs for Abstraction and Reasoning Corpus (ARC)

The Abstraction and Reasoning Corpus (ARC) poses a significant challenge to artificial intelligence, demanding broad generalization and few-shot learning capabilities that remain elusive for current deep learning methods, including large language models (LLMs). While LLMs excel in program synthesis, their direct application to ARC yields limited success. To address this, we introduce ConceptSearch, a novel function-search algorithm that leverages LLMs for program generation and employs a concept-based scoring method to guide the search efficiently. Unlike simplistic pixel-based metrics like Hamming distance, ConceptSearch evaluates programs on their ability to capture the underlying transformation concept reflected in the input-output examples. We explore three scoring functions: Hamming distance, a CNN-based scoring function, and an LLM-based natural language scoring function. Experimental results demonstrate the effectiveness of ConceptSearch, achieving a significant performance improvement over direct prompting with GPT-4. Moreover, our novel concept-based scoring exhibits up to 30\% greater efficiency compared to Hamming distance, measured in terms of the number of iterations required to reach the correct solution. These findings highlight the potential of LLM-driven program search when integrated with concept-based guidance for tackling challenging generalization problems like ARC.

HaCore: Efficient Coreset Construction with Locality Sensitive Hashing for Vertical Federated Learning

Vertical federated learning (VFL) trains model when the features of data samples are scattered over multiple clients. To improve efficiency, a promising approach is to find a coreset of the data samples and use it as a smaller training set. However, existing methods produce a large coreset when there are many clients and have long running time. To address these problems, we propose HaCore for efficient coreset construction in VFL setting. HaCore first employs locality sensitive hashing (LSH) to map features to bit signatures locally on the clients, and then merges the local signatures for k-medoids clustering. Data samples that correspond to the medoids are added to the coreset. The core idea is that the distance of original data samples can be approximated by the Hamming distance between their LSH-based bit signatures. To accelerate k-medoids, we utilize an inverted index to search the nearest medoid and a bit-counting method to quickly compute the aggregate distance from many signatures to a medoid. We evaluate HaCore on 5 datasets and compare with state-of-the-art coreset construction methods for VFL. The results show that HaCore accelerates the best-performing baseline by over 45x and matches the accuracy of training with all samples.

ConceptSearch: Towards Efficient Program Search Using LLMs for Abstraction and Reasoning Corpus (ARC) (Student Abstract)

The Abstraction and Reasoning Corpus (ARC) poses a significant challenge to artificial intelligence, demanding broad generalization and few-shot learning capabilities that remain elusive for current deep learning methods, including large language models (LLMs) (Chollet 2019). While LLMs excel in program synthesis, their direct application to ARC yields limited success. To address this, we introduce ConceptSearch, a novel function-search algorithm that leverages LLMs for program generation and employs a concept-based scoring method to guide the search efficiently. Experimental results demonstrate that ConceptSearch outperforms direct GPT-4 prompting, with our novel scoring function boosting efficiency by ~30% compared to the baseline Hamming distance scoring. Code at https://github.com/kksinghal/concept-search

Primitive idempotents of a-constacyclic codes over 𝔽q and their minimum Hamming distances

Primitive idempotents of <i>a</i>-constacyclic codes over 𝔽_<i>q</i> and their minimum Hamming distances

Sequence analysis of local indicators of spatio-temporal association for evolutionary pattern discovery

ABSTRACT The Local Indicators of Spatial Association (LISA) is one of the most widely used methods for identifying local patterns of spatial association in geographical elements. However, the dynamic trends of spatial-temporal (S-T) autocorrelation remain poorly understood, yet capturing these patterns is essential for analyzing the evolution of spatial processes. To fill the gap, we propose a novel S-T LISA methodology to automatically discover co-occurrences LISA subsequences over time by incorporating sequence analysis techniques. First, we extend the classical LISA to a dynamic context, and clarify the definition, properties, and classification of S-T LISA sequences. Second, we adopt an enhanced Hamming distance to quantify the similarity of LISA sequences, followed by hierarchical clustering to group similar LISA sequences. Next, an improved FP-Growth algorithm is applied to identify frequent patterns. Finally, we conduct experiments using grid-scale social media check-in records and city-scale carbon emission data to discover significant evolutionary patterns. The results verified the applicability of the proposed method in both human and physical geography. The proposed approach outperforms traditional S-T cube methods in its ability to automatically capture dynamic, complex, and transient S-T association trends as well as irregular outliers. The integration of sequence analysis with LISA statistics presented in this article provides an effective framework for identifying evolutionary patterns of S-T association.

An improved key scheduling for advanced encryption standard with expanded round constants and non-linear property of cubic polynomials

The advanced encryption standard (AES) offers strong symmetric key encryption, ensuring data security in cloud computing environments during transmission and storage. However, its key scheduling algorithm is known to have flaws, including vulnerabilities to related-key attacks, inadequate nonlinearity, less complicated key expansion, and possible side-channel attack susceptibilities. This study aims to strengthen the independence among round keys generated by the key expansion process of AES—that is, the value of one round key does not reveal anything about the value of another round key—by improving the key scheduling process. Data sets of random, low, and high-density initial secret keys were used to evaluate the strength of the improved key scheduling algorithm through the National Institute of Standards and Technology (NIST) frequency test, the avalanche effect, and the Hamming distance between two consecutive round keys. A related-key analysis was performed to assess the robustness of the proposed key scheduling algorithm, revealing improved resistance to key-related cryptanalysis.