Reviewerly/exHarmony huggingface dataset:
Fast Component Density Clustering in Spatial Databases: A Novel Algorithm

Clustering analysis is a significant technique in various fields, including unsupervised machine learning, data mining, pattern recognition, and image analysis. Many clustering algorithms are currently used, but almost all of them encounter various challenges, such as low accuracy, required number of clusters, slow processing, inability to produce non-spherical shaped clusters, and unstable performance with respect to data characteristics and size. In this research, a novel clustering algorithm called the fast component density clustering in spatial databases (FCDCSD) is proposed by utilizing a density-based clustering technique to address the aforementioned existing challenges. First, from the smallest to the largest point in the spatial field, each point is labeled with a temporary value, and the adjacent values in one component are stored in a set. Then, all sets with shared values are merged and resolved to obtain a single value that is representative of the merged sets. These values represent final cluster values; that is, the temporary equivalents in the dataset are replaced to generate the final clusters. If some noise appears, then a post-process is performed, and values are assigned to the nearest cluster based on a set of rules. Various synthetic datasets were used in the experiments to evaluate the efficiency of the proposed method. Results indicate that FCDCSD is generally superior to affinity propagation, agglomerative hierarchical, k-means, mean-shift, spectral, and density-based spatial clustering of applications with noise, ordering points for identifying clustering structures, and Gaussian mixture clustering methods.
The ASR Post-Processor Performance Challenges of BackTranScription (BTS): Data-Centric and Model-Centric Approaches

Training an automatic speech recognition (ASR) post-processor based on sequence-to-sequence (S2S) requires a parallel pair (e.g., speech recognition result and human post-edited sentence) to construct the dataset, which demands a great amount of human labor. BackTransScription (BTS) proposes a data-building method to mitigate the limitations of the existing S2S based ASR post-processors, which can automatically generate vast amounts of training datasets, reducing time and cost in data construction. Despite the emergence of this novel approach, the BTS-based ASR post-processor still has research challenges and is mostly untested in diverse approaches. In this study, we highlight these challenges through detailed experiments by analyzing the data-centric approach (i.e., controlling the amount of data without model alteration) and the model-centric approach (i.e., model modification). In other words, we attempt to point out problems with the current trend of research pursuing a model-centric approach and alert against ignoring the importance of the data. Our experiment results show that the data-centric approach outperformed the model-centric approach by +11.69, +17.64, and +19.02 in the F1-score, BLEU, and GLEU tests.
Density Peak Clustering Based on Relative Density under Progressive Allocation Strategy

In traditional density peak clustering, when the density distribution of samples in a dataset is uneven, the density peak points are often concentrated in the region with dense sample distribution, which is easy to affect clustering accuracy. Under the progressive allocation strategy, a density peak clustering algorithm based on relative density is proposed in this paper. This algorithm uses the K-nearest neighbor method to calculate the local density of sample points. In addition, in order to avoid the domino effect during sample allocation, a new similarity calculation method is defined, and a progressive allocation strategy from near to far is used for the allocation of the remaining points. In order to evaluate the effectiveness of this algorithm, comparative experiments with five algorithms were carried out on classical artificial datasets and real datasets. Experimental results show that the proposed algorithm can achieve higher clustering accuracy on datasets with uneven density distribution.
Discourse Markers in an Online Community of Buhînën People

One of the language communication challenges is how it works as speakers use it. Some words may not change their semantic meaning, but when it is meant to say on speaker’s intention, it impacts the context; these particles are discourse markers. It has been used in everyday interaction, face-to-face communication, writing, and an online environment. This compels the researcher to investigate further the occurrences of the discourse markers in Buhînën People in an online community. This study centralized how discourse markers maintain their status of cohesion and interpersonal in computer-mediated communication. This qualitative study was based on Gustilo and Palacio’s (2016) study on discourse particles. Discourse markers were categorized into two: textual and relational categories. The corpus of 2,000 Facebook posts, primarily texts, was the data collected from the most numbered population on the Facebook online group of Buhînëns. Then, the survey was conducted to gather data about the pragmatic relation of relational discourse markers. The data revealed that the most hits were the discourse marker ‘na’ for the textual category, whereas ‘po’ for the relational category. It also revealed that more textual discourse markers were found. The functions proved the idea of metalingual function (Maschler &amp; Schiffrin, 2015), as it has many functions in both categories, including the micro-function of relational discourse markers. Furthermore, the study also answered the importance of the discourse markers in pragmatics and computer-mediated communication, through which they served both their primary roles as structural and interpersonal.
Leadership skills development of modern children in the digital transformation era

The research is dedicated to the problem of the leadership skills development of modern children under the influence of digitalization, the modern phenomenon of "digital childhood". It is noted that today, in order to develop the leadership qualities of modern children, a number of pedagogical technologies are used in the educational process, which makes the socialization of a child more successful. The emphasis is put on the question of which technologies are of the greatest interest as well as are more convenient in a modern educational process. It is recalled that digitalization is the main trend of Russian education, the risks, that cannot be avoided in the era of technologization and digital transformation, are defined. An explanation to the family education importance in the process of becoming a child's leader is provided, and we also get acquainted with the term of "digital socialization", which is inextricably linked with the peculiarities of the children's leadership skills development in the era of digital transformation.
Research on midcourse target tracking method of ballistic missile

The midcourse of ballistic missile is the longest section of ballistic missile flight time. Accurate tracking of ballistic missile in this section can gain more time for warhead identification and determine the best interception point for ballistic missile defense system. In this paper, the method of midcourse target tracking of ballistic missile is studied. The motion acceleration of the mid-course target of ballistic missile in EUN coordinate system and spherical coordinate system is given, and the state equation and measurement equation in the mixed coordinate system and spherical coordinate system are established. The key functions are expressed explicitly, and the UKF algorithm is used to filter and track the ballistic target. Simulation analysis and comparison of the influence of the two coordinate systems on tracking performance are conducted. Simulation analysis compared the effect on tracking performance under two kinds of coordinate system.
Building layover detection in SAR images via image translation

Conventionally image translation is used to convert synthetic aperture radar (SAR) images to optical ones to increase interpretability. Due to the different imaging natures of SAR range sensing and optical directional projection, it is within the expectation that some kinds of SAR images could be reasonably translated into optical ones, while others not. Arguably, less good patches in the translated image could act as a potential indicator of salient differences of the two imaging mechanisms. Following this line of thought, considering in urban SAR images, the layover is mainly caused by buildings, we proposed a method to detect building layovers in SAR images by evaluating the correlation of the original SAR image and the translated one by the cycle-translation, that is, from SAR to optical to SAR translation. Because the presence of building is the main factor of the layover phenomenon in urban SAR images, building layover areas in SAR images are expected to be less correlated, or they should have a low correlation coefficient. Preliminary experiments validate our underlying principle and method.
Integration and construction of gas monitoring and warning visualization platform

With the in-depth implementation of national environmental protection policies, gas will be more widely used in our lives, which requires better design to upgrade the gas safety. On the basis of the project, our laboratory constructed a gas monitoring and warning visualization platform through a variety of front-end and back-end technologies, enabling managers to conduct real-time monitoring of equipment status in different gas stations through web pages. The system focuses on the collection and analysis of video information, which can effectively identify the identity and action of the personnel entering the gas station, and then mark the personnel and issue an early warning on the video window of the visual page of the system, thus improving the management ability of the gas company of each gas station.
Age invariant face recognition based on convolutional neural networks

Age-invariant face recognition (AIFR) can be used in law enforcement, security, and citizenship verification. Face recognition as age changing is huge and tough task for human. In this paper, the features of faces across ages will be studied and cross-age face recognition will be accomplished, based on the Convolutional Neural Network (CNN) algorithm and other modules. This paper aims to reduce the impact of age on face recognition and modify the model based on previous scholars' research. Open-source datasets will be used, and then a deep learning model will be exploited to find results on the ranking 1 accuracy of about 90%. The results and models comparison will be discussed in the paper.
Management optimization of video system in the community

With the modernization development of the city, the community policing management has become an advanced management mode, and under this management mode, the community video system plays an important role in this management mode. To ensure the normal progress of community governance requires the efficient and high-quality operation of the community video system. In order to ensure the smooth and efficient development of the community policing work, we need to propose the necessary solutions for the color bias phenomenon of the monitoring images. This paper mainly puts forward the solution of this problem from four aspects: resource sharing, technology governance, hardware guarantee and emergency plan, so that such problems can be encountered in the public security practice and have a three-dimensional and rapid response mechanism to deal with such problems. In this paper, the histogram statistical method is used to detect the color bias of the monitoring images. The advantages of this method are the intuition and the rich detection information.
An explicit construction of minimal support AME states

Absolutely maximally entangled (abbreviation AME) states are pure multipartite states that, when reduced to half or less of the sites, the maximum entropy mixed state is obtained, which have found significant applications in teleportation and quantum secret sharing. In this paper, we consider the sufficient conditions for the existence of the minimal support AME (<i>n, q</i>) state, and present a systemically explicit construction about minimal support AME states, then several minimal support AME states are drawn by employing GRS codes, Singleton arrays and their deformed arrays.
Feature extraction and classification method of electrooculogram based on variational modal decomposition

With the progress of science and technology, human-computer interaction technology has become a research hotspot. Many new interaction methods have been developed. As an important organ to receive information, eye plays an important role in human-computer interaction. In this paper, an electrooculogram acquisition system is designed by using five electrodes to measure the amplitude of electrooculogram. The 3Hz and 8Hz components of electrooculogram are extracted by variational modal decomposition. The maximum, minimum, distance between maximum and minimum, mean and standard deviation of these frequency components are used as features. KNN, SVM, CART and Bagging classifiers are used to classify electrooculogram. In the two-dimensional coordinate system, the highest accuracy of the classification of 44 eye rotation angles is 88.40%, which is 12.50% higher than the accuracy of only using the time domain features. It proves the effectiveness of the proposed method and provides a richer interaction mode for human-computer interaction.
Design and implementation of hash function based on chaos map

The main objective of this thesis is to make the traditional SHA-256 algorithm computationally efficient and secure. By improving the iterative structure of the SHA-256 algorithm, we change the output of the algorithm from eight 32-bit groupings to four 64-bit groupings, so that the 16 iterations per round of the main loop of the algorithm become eight iterations, thus reducing a total of 32 iterations in four rounds, which in turn improves the computational efficiency of the algorithm, and further improves the avalanche property of the LHF by constructing a logistic chaos The avalanche property of the LHF is further improved by constructing logistic chaos mappings with variable parameters during the message computation. Finally, it is proved through experiments that the probability of bad points in LHF is reduced by 1% compared with MD5, and the probability of bad points in LHF is reduced by 0.3% compared with SHA-256, so it has excellent avalanche and diffusion effect, and also in the performance analysis, the computation efficiency of LHF is accelerated by 53.13% compared with SHA-256 and 14.77% compared with MD5, so it achieves the purpose of speeding up the computational efficiency.
Based on Beidou short message of high-precision landslide early warning information release technology research

To the high accuracy real-time landslide early warning information sent to the disaster area, reduce the landslide caused loss of life and property of people, design a kind of based on Beidou short message communication mode to release the method of high-precision landslide early warning information, optimize the landslide early warning information release, the standard of Beidou short message protocol and CAP (Common Alerting Protocol) combined general alarm agreement, enrich and perfect the early warning information content, high precision of landslide early warning information for the package transmission, and automatically by the Beidou terminal receipt Beidou short message warning message, implements the information based on Internet and Beidou short message communication connectivity, provides the way of warning issued emergency measures, improve the timeliness and reliability of the landslide early warning information release.
An interpretable classification model of breast tumors with tabular mammography data

Breast cancer is the most prevalent malignant disease among cancers, and its mammography and examination reports are an important basis for clinicians to judge the nature of breast tumours. Current research based on mammography reports has focused on the natural language classification models, ignoring the practical problems of the semantic hierarchy in examination reports and the imbalance of benign and malignant categories. To address this issue, we propose an interpretable classification model of breast tumors with tabular mammography data (STaNet). The model is based on a semantic tree to structure the image presentation part of the mammography report, allowing it to focus on the disease information carried at the semantic level of the report better. Meanwhile, we dynamically conbine the focus loss function parameters with the category loss, allowing the model to dynamically adjust its focus category as it learns. We evaluated our model using real-world mammogram report data and compared the model with other excellent models. The experimental results show that the accuracy of our proposed model is better than other models and its interpretable results are in line with the diagnostic criteria of pathologists.
Research progress and frontier analysis of fine-grained image classification based on CiteSpace

With the growth of image classification models and the development of deep learning, the level of computer image classification has basically surpassed that of humans, but the level of fine-grained computer image classification is still weaker than that of humans, so fine-grained image classification is currently given a lot of attention. This paper carries out visualized analysis for the fine-grained image classification in terms of cooperation, scientific research, research trend and other aspects by CiteSpace software, and also points out that fine-grained image classification relies on CNN while looking into Transformer. However, fine-grained image classification has the problems of model reliance on prior knowledge (CNN), lack of sufficient data (Transformer), and lack of local relevance (CNN/Transformer), which can be solved by reducing the complexity of the model, transfer learning data augmentation, combination of CNN with Transformer, etc. On this basis, this paper indicates that Swin-Transformer has good generalization, hierarchy and translation invariance, and will become the main research direction for fine-grained image classification in the future.
Research on adaptive image enhancement algorithm in laser stripe extraction

In line structured light vision measurement, an adaptive image enhancement algorithm is proposed to address the problem that the conventional image enhancement algorithm is ineffective in enhancing the laser stripe features in the unevenly illuminated images acquired by the system. The algorithm separates the channels in HSV color space, processes the separated S and V channels separately using an iterative adaptive histogram equalization algorithm to enhance the image saturation and brightness, and merges the equalized S<sub>1</sub> and V<sub>1</sub> channels with the original H channel to convert the image to RGB color space to complete the image enhancement. The experimental result shows that the information entropy index value S of the proposed algorithm is higher than those of other comparative algorithms, and the algorithm performs better in laser stripe image enhancement with better robustness.
Research on quasi-cylinder labels unfolding method based on structured light

For the expansion problem of quasi-cylinder labels with unequal radii, this paper proposes a cylindrical-like label surface expansion algorithm based on structured light vision. First, a structured light vision system is built by using an area scan camera and a cross laser. Second, the cross-laser point cloud of the quasi-cylinder label is obtained, and all the label point clouds are reconstructed according to the obtained laser point cloud. Finally, using the camera imaging model and the acquired images, the surface unfolding of the quasi-cylinder label is realized. The experimental results show that the algorithm takes only 150ms to realize the surface expansion of the quasi-cylinder label. And this algorithm does not require complex pose optimization, so the efficiency of the algorithm is further improved.
Arnold logistic sequence encryption for non-isometric images

Information encryption is an important part of information transmission in modern society. At present, image encryption algorithms face problems such as applicability and security, which need to be solved urgently. This paper proposes an image encryption algorithm based on Arnold-Logistic sequence encryption, which uses the idea of Euler concatenation of fractions to chunk the non-equal-length images in order to perform Arnold transform. Then the random key stream is obtained for the use of Logistic sequences to perform the heteroskedastic operation on the image pixels. After experiments, the algorithm in this paper is proved to be effective in improving the cipher image security, extending the applicability of the algorithm, and also greatly reducing the computing time and space occupation.
Research on target location and TMA of bistatic sonar continuous detection

Aiming at the multistatic continuous wave detection problem, the target positioning and motion analysis of bistatic continuous detection are studied. This paper discusses the multi-base continuous detection signal model, target positioning accuracy and target motion factors. The algorithm uses the continuous single-frequency signal as the bistatic continuous detection waveform, and analyzes the target location method. The extended Kalman filter is used to provide the positioning accuracy. The target motion elements of the bistatic sonar system are further discussed, and the Monte Carlo method is used to simulate and analyze the method in this paper. The simulation results show that the pure azimuth positioning method of bistatic continuous detection has better positioning accuracy than the traditional sonar. The main factor affecting the target velocity vector is the estimated radial velocity of the transceiver station.
Research on mission planning and data security of cross-domain dynamic authorization based on blockchain

The research of cross-domain mission planning and data security is an important component of developing multi-level cross-domain collaboration. The drawbacks and problems of traditional cross-domain mission planning include geographical physical isolation, very different authentication models, and transmission hierarchy. In order to achieve efficient cross-domain mission planning and meet the collaboration needs of different domains, a cross-domain dynamic authorization mission planning scheme is proposed based on blockchain technology. It first constructs a cross-domain mission collaboration organization architecture that enables each mission domain to join the consortium chain network with a unified authentication, then we designed a method process of dynamic authorization and a smart contract of cross-domain mission collaboration, which can transfer and control the authority of cross-domain mission planning and ensure the supervisibility and traceability of each process of mission collaboration. The experiments show that the scheme can achieve effective control of the cross-domain mission implementation process, and meet the timely requirements of cross-domain mission planning by running efficiently.