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

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

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]

Currently, coal mining faces the uncertainty of the risk of coal and gas outbursts and inaccurate prediction results. Owing to this, an artificial immune algorithm (AIA) was developed for coal and gas outburst prediction based on the Hamming distance (HD) calculation method of antibody and antigen affinity called the Hamming distance artificial intelligence algorithm (HDAIA). The correlation matrix of coal and gas outburst indicators was constructed using the interpolation function in the algorithm. The HD algorithm was used to obtain the affinity between the antibody and antigen, and the minimum HD was screened to obtain the prediction result. The collected dynamic data of the drilling cuttings gas desorption index K1 and the drilling cuttings weight S during the excavation process of the 11,192-working face of a coal mine in Guizhou Province, China, were used as prediction indices. The results indicate that the prediction result of the HDAIA for the risk of coal and gas outbursts is consistent with the actual risk of outbursts, and it has a good prediction of the risk of coal and gas outbursts. The HDAIA can be used as a novel method for predicting the risk of coal and gas outbursts.

The orthogonal time frequency space (OTFS) modulation technique can provide reliable communication in time-varying channels. Due to the dispersive characteristics of underwater acoustic channels, this paper proposes an OTFS-IM underwater acoustic communication system based on Hamming distance optimization to reduce the impact of dispersion in underwater acoustic communication. Firstly, the OTFS-IM underwater acoustic communication system is introduced, which introduces index modulation into the Delay–Doppler (DD) domain to make the OTFS system have stronger anti-Delay–Doppler capability. In contrast, since there is index sequence redundancy in a specific index combination, a Hamming distance optimization model is used to eliminate the redundant combination in the specific index combination sequence and further improve the bit error rate performance of the system. In addition, the Hamming distance optimized OTFS-IM underwater acoustic communication system is verified by simulation analysis. The results show that the proposed Hamming distance optimized OTFS-IM can achieve more reliable bit error rate performance. [ABSTRACT FROM AUTHOR]

The proposal of the “dual carbon” goal is a deep reflection of the inherent requirements for achieving sustainable development, and has become an important constraint and policy direction for the development of all industries and sectors during the 14th Five Year Plan period and even longer. Tourism destinations are important spatial carriers for the tourism industry to achieve the “dual carbon” goals, and the tourism environment is the foundation for the sustainable development of tourism activities and an important prerequisite for the sustainable development of tourism destinations. Therefore, in the context of “dual carbon”, evaluating the current status and future development potential of sustainable carrying capacity of destination tourism environment is of great practical significance for achieving the “dual carbon” goal. The sustainable potential bearing capacity evaluation of tourism environment is a multiple-attribute group decision-making (MAGDM) problem. Recently, the Evaluation based on Distance from Average Solution (EDAS) technique has been employed to manage MAGDM issues. The intuitionistic fuzzy sets (IFSs) are utilized as a tool for portraying uncertain information during the sustainable potential bearing capacity evaluation of tourism environment. In this paper, the intuitionistic fuzzy number EDAS (IFN-EDAS) technique is cultivated to manage the MAGDM based on Hamming distance and Logarithmic distance under IFSs. Finally, a numerical study for sustainable potential bearing capacity evaluation of tourism environment is supplied to validate the proposed technique. The main contributions of this paper are outlined: (1) the EDAS technique has been extended to IFSs based on Hamming distance and Logarithmic distance; (2) the CRITIC technique is utilized to derive weight based on Hamming distance and Logarithmic distance under IFSs. (3) the IFN-EDAS technique based on Hamming distance and Logarithmic distance is founded to manage the MAGDM based on the Hamming distance and Logarithmic distance under IFSs; (4) a numerical study for sustainable potential bearing capacity evaluation of tourism environment and some comparative analysis is supplied to validate the proposed technique.

In this paper, we discussed binary fuzzy codes over a vector space F2n by relating classical codes with the probability of a binary symmetric channel (BSC) for receiving a sent codeword correctly. We used the weight of error patterns between a received word and the possible sent codewords to define fuzzy words over n-dimensional vector space F2n, and used it to define binary fuzzy codes. We also added some properties of Hamming distance of binary fuzzy codes, and the bounds of a Hamming distance of binary fuzzy codes for p=1/r, where r⩾3, and r∈Z+, are determined. Finding Hamming distance of binary fuzzy codes is used for decoding sent messages on a BSC.

Reducing the distance between magnetic islands is essential for increasing the areal density in bit-patterned media recording (BPMR). Consequently, significant two-dimensional (2D) interference emerges, encompassing inter-symbol interference (ISI) and inter-track interference (ITI) along the cross- and down-tracks, respectively. To address the 2D interference, modulation with non-isolated patterns can be employed to mitigate the interference from neighboring bits. In previous studies, authors relied on the Hamming code to propose a non-isolated modulation code with a minimum Hamming distance (MHD) of 3. However, for encoding, authors utilized a mapping method with a large look-up table, resulting in prolonged delay for the decoder. Therefore, in this paper, we proposed a generator matrix for creating the Hamming code with an MHD of 3, thereby generating reverse codewords in the look-up table. Consequently, only half of the look-up table needs to be stored, leading to reduced complexity and delay time in the decoder for non-isolated modulation code with an MHD of 3. Simulation results demonstrate that our proposed model can enhance bit error rate performance and decrease delay time compared to previous studies.

The use of a Controller Area Network (CAN) bus in the automotive industry for connecting electronic control units (ECUs) poses security vulnerabilities due to the lack of built-in security features. Intrusion Detection Systems (IDSs) have emerged as a practical solution for safeguarding the CAN bus. However, developing an effective IDS for in-vehicle CAN buses encounters challenges in achieving high precision for detecting attacks and meeting real-time requirements with limited computational resources. To address these challenges, we propose a novel method for anomaly detection on CAN data using windowed Hamming distance. Our approach utilizes sliding windows and Hamming distance to extract features from time series data. By creating benchmark windows that span at least one cycle of data, we compare newly generated windows with recorded benchmarks using the Hamming distance to identify abnormal CAN messages. During the experimental phase, we conduct extensive testing on both the public car-hack dataset and a proprietary dataset. The experimental results indicate that our method achieves an impressive accuracy of up to 99.67% in detecting Denial of Service (DoS) attacks and an accuracy of 98.66% for fuzzing attacks. In terms of two types of spoofing attacks, our method achieves detection accuracies of 99.48% and 99.61%, respectively, significantly outperforming the methods relying solely on the Hamming distance. Furthermore, in terms of detection time, our method significantly reduces the time consumption by nearly 20-fold compared to the approach using deep convolutional neural networks (DCNN), decreasing it from 6.7 ms to 0.37 ms.

Current recommendation algorithms based on hashing technology commonly uses Hamming distance to indicate the similarity between user hash code and item hash code,while it ignores the potential difference information of each bit dimension.Therefore,this paper proposes a differential Hamming distance,which by calculating the dissimilarity between hash codes to assign bit weights.This paper designs a variational recommendation model for dissimilarity Hamming distance.The model is divided into a user hash component and an item hash component,which are connected by variational autoencoder structure.The model uses encoder to generate hash codes for user and items.In order to improve the robustness of the hash codes,we apply a Gaussian noise to both user and item hash coeds.Besides,the user and item hash codes are optimized by differential Hamming distance to maximize the ability of the model to reconstruct user-item scores.Experiments on benchmark datasets demonstrate that the proposed algorithm VDHR improves 3.9% in NDCG and 4.7% in MRR compared to the state-of-the-art hash recommendation algorithm under the premise of constant computational cost.

Let A (G) be the adjacency matrix of a graph G. Let s (v) denote the row entries of A (G) corresponding to the vertex v of G. The Hamming distance between the strings s (u) and s (v) is the number of positions in which s (u) and s (v) differ. In this paper, we study the Hamming distance between the strings generated by the adjacency matrix of subgraph complement of a graph. We also compute sum of Hamming distances between all pairs of strings generated by the adjacency matrix of G ⊕ S. [ABSTRACT FROM AUTHOR]

The use of a Controller Area Network (CAN) bus in the automotive industry for connecting electronic control units (ECUs) poses security vulnerabilities due to the lack of built-in security features. Intrusion Detection Systems (IDSs) have emerged as a practical solution for safeguarding the CAN bus. However, developing an effective IDS for in-vehicle CAN buses encounters challenges in achieving high precision for detecting attacks and meeting real-time requirements with limited computational resources. To address these challenges, we propose a novel method for anomaly detection on CAN data using windowed Hamming distance. Our approach utilizes sliding windows and Hamming distance to extract features from time series data. By creating benchmark windows that span at least one cycle of data, we compare newly generated windows with recorded benchmarks using the Hamming distance to identify abnormal CAN messages. During the experimental phase, we conduct extensive testing on both the public car-hack dataset and a proprietary dataset. The experimental results indicate that our method achieves an impressive accuracy of up to 99.67% in detecting Denial of Service (DoS) attacks and an accuracy of 98.66% for fuzzing attacks. In terms of two types of spoofing attacks, our method achieves detection accuracies of 99.48% and 99.61%, respectively, significantly outperforming the methods relying solely on the Hamming distance. Furthermore, in terms of detection time, our method significantly reduces the time consumption by nearly 20-fold compared to the approach using deep convolutional neural networks (DCNN), decreasing it from 6.7 ms to 0.37 ms. [ABSTRACT FROM AUTHOR]

Daur Ulang Text didefinisikan sebagai pemanfaatan sumber tulisan yang ada untuk penulisan sebuah teks baru. Persentase penggunaan ulang teks dari sumber sebelumnya sangatlah bervariasi. Jika prosentase penggunaan tersebut tinggi dan berasal dari sebuah sumber, maka teks yang baru menjadi teks duplikat atau hampir duplikat dengan teks sumbernya. Meskipun beberapa genre teks bisa diterima, keberadaan teks duplikat dan hampir dupilkat ini menyebabkan ketidak-efisienan penyimpanan dan pencarian. Untuk itu diperlukan sebuah system deteksi kemiripan teks yang akan mengidentifikasi teks mana saja yang dupilkat dan hampir duplikat. Untuk itu, penelitian ini berfokus pada deteksi kemiripan teks dengan mengaplikasikan algoritma Simhash. Algoritma ini digunakan untuk menghasilkan fingerprint dokumen yang berfungsi sebagai fitur dokumen yang digunakan sebagai dasar pembanding tingkat kemiripan teks. Kemiripan sebuah teks terhadap teks lainnya diukur dengan menggunakan jarak Hamming. Dalam ekperimen yang difokuskan pada dokumen duplikat dan hampir duplikat, tingkat Recall dokumen cukup tinggi yakni 80%. Ini berarti bahwa sistem yang dikembangkan mampu menemenukan pasangan dokumen duplikat dengan baik.