Reviewerly/exHarmony huggingface dataset:
A Method for Football Players Detection on the Soccer Field by Integrated Image Processing Techniques

the player detection algorithm in a soccer field image is an important step for an offside situation detection. A difficulty of player detection occur when the player is overlap with the crowd of cheering. In this paper, we propose a new method for football player detection on the soccer field by integrated image processing techniques. This method consists of two steps. Firstly, the soccer field area is segmented from the background. This step is used to avoid the error of player detection from overlap problem between player and crowd of cheering. Finally, the process to detect players in the soccer field from Morphological. To test the performance of the proposed method, the images used to test in this research have 150 images from three football matches. The detected players compared with the total players on the field by visual. Detection rate is 85.943% and the accuracy of the player detection process is 76.63%. The result show that the proposed method is satisfactory in order to continue for performance improvement.
Blockchain Technology and Impacts on Potential Industries

Blockchain technology is frequently referred to as the fourth industrial revolution, which will change the world. Blockchain technology sometimes referred to as distributed ledger technology creates an ecosystem that is decentralized, dispersed, and devoid of central authority. Since Bitcoin's introduction, research into non-financial use cases has continued to expand the technology's usefulness. Through categorization and integrity verification in both the industry and processing terminals, the blockchain paradigm regulates data collection and dissemination instances. From the Bitcoin digital currency system to more contemporary uses, the origin of this technology is traced. This paper presents essential ideas about Blockchain and offers our perspective on the challenges, the future changes, and the predictable effect of Blockchain. Blockchain has created an immense pool of opportunities for every field of life, e-commerce, supply chain, sustainable smart cities, or adoption of e-governance. The impact on various fields by use of Blockchain is discussed, also raising the research investigation questions.
From fellow traveller to eco-warrior: the translation and reception of Thoreau in China

Abstract First published in 1854, Walden , by Henry David Thoreau, has been translated into many languages. The last three decades have witnessed a growing number of Chinese versions of Walden , together with an upsurge of interest in this classic American book. Adopting a descriptive and diachronic approach, this article surveys the different Chinese versions of Walden , the studies by Chinese scholars on Thoreau/ Walden , as well as eco-writings inspired by Thoreau/ Walden in China, with the purpose of investigating the translation and reception of Thoreau in China from three perspectives: those of translators, literary scholars and writers. Thoreau was initially understood by his Chinese translators as a foreign thinker whose work endorses traditional Chinese values such as the celebration of Nature and Tianrenheyi 天人合— (or “harmony between Nature and man”), and Walden was valued mainly for its perceived alignment with a “native” component in Chinese philosophy and a multifaceted figure in China. Later, this writing was regarded as a product of the complex integration of Chinese and American thinking in the eyes of Chinese literary scholars. Most recently, Thoreau has been transformed into a multifaceted figure in China, one who has enjoyed a great influence upon Chinese eco-writers. In some respects, the rising popularity of Thoreau in China mirrors his increasing popularity in the United States; however, the evolving reception of his work by translators, literary scholars and writers in China may diverge in detail from his critical and popular reception in his native country.
Semisupervised Bacterial Heuristic Feature Selection Algorithm for High-Dimensional Classification with Missing Labels

Feature selection is a crucial method for discovering relevant features in high-dimensional data. However, most studies primarily focus on completely labeled data, ignoring the frequent occurrence of missing labels in real-world problems. To address high-dimensional and label-missing problems in data classification simultaneously, we proposed a semisupervised bacterial heuristic feature selection algorithm. To track the label-missing problem, a k-nearest neighbor semisupervised learning strategy is designed to reconstruct missing labels. In addition, the bacterial heuristic algorithm is improved using hierarchical population initialization, dynamic learning, and elite population evolution strategies to enhance the search capacity for various feature combinations. To verify the effectiveness of the proposed algorithm, three groups of comparison experiments based on eight datasets are employed, including two traditional feature selection methods, four bacterial heuristic feature selection algorithms, and two swarm-based heuristic feature selection algorithms. Experimental results demonstrate that the proposed algorithm has obvious advantages in terms of classification accuracy and selected feature numbers.
An efficient differential privacy federated learning scheme with optimal adaptive client number K

Federated Learning (FL) protects users’ privacy by only uploading the training result instead of gathering all the private data. However, achieving the desired model performance often requires a large number of iterations of parameter transfer between client and central server. Currently, selecting a fixed number of clients to participate in training can slightly reduce the communication overhead during model training, but ignore the impact on model training accuracy. In this paper, we propose an adaptive chosen client number <i>K</i> scheme, which can give a better tradeoff between accuracy and cost. Firstly, through experiments, we find that increasing extracted clients’ number <i>K</i> can reduce iterations’ number <i>T</i>, but after <i>K</i> increases to a certain extent (𝐾<sub>1</sub>), <i>T</i> will no longer reduce significantly. Similarly, increasing <i>K</i> can further improve the accuracy of model training, but <i>K</i> is large enough (𝐾<sub>2</sub> ≥ 𝐾<sub>1</sub>), the accuracy will also no more be improved remarkably. Thus, [𝐾<sub>1</sub>,𝐾<sub>2</sub>] is the optimal range. Secondly, we conduct experiments on different datasets with different number of clients, and find that the optimal client’s number growth rate <i>q’</i> for different conditions is 0.02. According to the experimental results, we set the initial <i>K</i> to be 𝐾<sub>1</sub> for the optimal <i>T</i>, when the model update magnitude in two adjacent iterations is less than a threshold, the number of clients participating in training will increase by <i>q’</i> to speed up the convergence until <i>K</i> reaches K<sub>2</sub>, otherwise it will remain unchanged. Finally, we use our algorithm to improve present FL algorithms. Through experiments, we demonstrate that our algorithm is suitable for existing differential private FL algorithms.
Joint Dual-Structural Constrained and Non-negative Analysis Representation Learning for Pattern Classification

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Research on holographic display and technology application of art museum based on immersive design

Abstract With the rapid development of science and technology, artificial intelligence digital technology has gradually entered human life and combined with art. It has become a major trend of future exhibitions to create immersive exhibitions through the intervention of digital technology and artificial intelligence in art museums. The immersive art exhibition is to change the sensory conditions of the audience through the intervention of digital media, from the original visual experience to the fusion of various senses such as “sight, hearing and touch”, so that the audience can enter a new world of works experience. the immersive exhibition composed of the combination of technology and art has become an important exhibition method to attract audiences. The creation of immersive exhibitions relies on the application of technologies such as Front-projected Holographic Display and Augmented Reality. At present, immersive exhibitions around the world are emerging in an endless stream. Under the general trend of information exhibition, how to combine works of art with digital media and endow them with connotation, and how digital technology is involved in the exhibition to form the final immersive effect, are issues worth considering at present.
Ultra-short-term PV prediction based on LSTM with a multi-head attention mechanism

Abstract With the increasing penetration and installation of renewable energy such as photovoltaic (PV) generation, ultra-short-term PV output prediction is necessary to guarantee the stability of the power system. However, the traditional methods cannot capture the important features of PV power data, and prediction accuracy cannot be guaranteed. Focusing on these problems, this paper presents a novel PV prediction method based on LSTM with a multi-head attention mechanism. The mechanism can make the matrix operation run in parallel. At the same time, the model assigns weight to different features of the input data to improve the prediction. To show the effectiveness, this paper constructs four scale types for PV power prediction. Case studies show that the multi-head attention mechanism can improve the prediction performance, and the prediction accuracy decreases following the increase of time scale.
The effectiveness of different machine learning algorithms in classifying pulsar stars and the impact of data preparation

Abstract With massive improvements in technology, astronomers have been uncovering impressive findings of the mysteries of the universe. At the same time, new technology is able to do more and more of the tasks usually given to people, especially when it comes to handling big pieces of data. As such, more jobs are being replaced by machines in the modern industrial age, which may very well include the jobs of astronomers. To investigate the potential of machine learning in astronomy and its effect on the livelihoods of astronomers, this paper aims to demonstrate the capabilities of machine learning algorithms in completing tasks usually done by astronomers, which in this case the identifying pulsar stars. Astronomers study these stars extensively to learn about extreme states of matter and explore different solar systems. This paper introduces various classification algorithms to perform the task of identifying pulsar stars from data of radio wave emissions in the universe. Two distinct methods of dealing with missing values were also used. Once the algorithms were applied to the dataset, the results were recorded and analyzed, mainly focusing on the accuracy of each model. From the results, all the models yielded accuracies above 90%. In addition, the models performed better on average with data preparation using the drop method compared to the average value method. These results provide extensive evidence that machine learning can perform certain tasks commonly performed by astronomers themselves. At the moment they cannot replace astronomers, but in the future, it has a possibility.
Experimental Study on Oil Colour Grading Based on Uniform Colour Space

Abstract Though the Lovibond colorimetric method is effective for determining oil colour, it is a visual comparison method which relies on manual visual reading. As a result, this method is vulnerable to the influence of subjective factors of testers and is of low accuracy. In this paper, the original index of oil colour grading based on Lovibond colour space is changed to the index of oil colour grading based on uniform colour space, and a new method is proposed to determine oil colour by spectrophotometry. The experimental results show that there is a strong correlation between Lovibond red value and the chroma index a *, and a strong correlation between Lovibond yellow value and the chroma index b *. So the method greatly improves the detection efficiency and avoids a series of disadvantages of the visual method.
A two-time-level model for mission and flight planning of an inhomogeneous fleet of unmanned aerial vehicles

Abstract We consider the mission and flight planning problem for an inhomogeneous fleet of unmanned aerial vehicles (UAVs). Therein, the mission planning problem of assigning targets to a fleet of UAVs and the flight planning problem of finding optimal flight trajectories between a given set of waypoints are combined into one model and solved simultaneously. Thus, trajectories of an inhomogeneous fleet of UAVs have to be specified such that the sum of waypoint-related scores is maximized, considering technical and environmental constraints. Several aspects of an existing basic model are expanded to achieve a more detailed solution. A two-level time grid approach is presented to smooth the computed trajectories. The three-dimensional mission area can contain convex-shaped restricted airspaces and convex subareas where wind affects the flight trajectories. Furthermore, the flight dynamics are related to the mass change, due to fuel consumption, and the operating range of every UAV is altitude-dependent. A class of benchmark instances for collision avoidance is adapted and expanded to fit our model and we prove an upper bound on its objective value. Finally, the presented features and results are tested and discussed on several test instances using GUROBI as a state-of-the-art numerical solver.
Recursive Least-Squares Wiener Consensus Filter and Fixed-Point Smoother in Distributed Sensor Networks

Distributed Kalman filter (DKF) is classified into the information fusion Kalman filter (IFKF), i. e. the centralized Kalman filter (CKF), and the Kalman consensus filter (KCF) in distributed sensor networks. The KCF has the advantage to improve the estimate of the state at the sensor node uniformly by incorporating the information of the observations and the filtering estimates at the neighbor nodes. In the first devised KCF, a user adjusts the consensus gain. This paper designs the recursive least-squares (RLS) Wiener consensus filter and fixed-point smoother that do not need to be adjusted in linear discrete-time stochastic systems. In addition to the observation equation at the sensor node, an observation equation is introduced excessively. Here, the new observation is the sum of the filtering estimates of the signals at the neighbor nodes of the sensor node. Thus, it is interpreted that the RLS Wiener consensus estimators incorporate the information of the observations at the neighbor nodes indirectly because the observations are used in the calculations of the filtering estimates. A numerical simulation example shows that the proposed RLS Wiener consensus filter and fixed-point smoother are superior in estimation accuracy to the RLS Wiener estimators.
Centrality measure and visualization technique for multiple-parent nodes of earthquakes based on correlation-metric

Abstract In this paper, we address the problem of earthquake declustering, and propose a k -nearest neighbors approach based on the selection of multiple-parent nodes with respect to each of the given earthquakes, which can be regarded as a natural extension of the conventional correlation-metric method based on the selection of a single-parent node. Based on this approach, we develop a centrality measure that exploits link weight assigned by a logarithmic-distance scheme and a technique of individually visualizing each set of child nodes with respect to given target earthquakes. For experimental evaluation, we used an earthquake catalog covering Japan and selected 24 earthquakes that caused considerable damage or casualties. We first show that our proposed centrality measure using a logarithmic-distance scheme can rank these 24 major earthquakes higher than four link-weighting schemes (i.e., uniform, magnitude, inverse-distance, and normalized-inverse-distance weighting) and conventional single-parent selection. We then show that unlike the conventional approach to simultaneously visualizing all the events in the catalog, our proposed technique can produce a naturally interpretable classification result for these 24 major earthquakes, by individually visualizing each set of the first to k -th child nodes with different colored markers plotted in the directly interpretable spatio and temporal metrics. As a consequence, we confirm that our approach based on multiple-parent selection is vital and promising.
Opportunities and Challenges in Vehicle Tracking: A Computer Vision-Based Vehicle Tracking System

This study proposes a tracking system for the transportation field utilizing state-of-the-art detection heads and tracking algorithm. We utilize detection heads (YOLO5, FRCNN, DETR) with tracking algorithms (ByteTrack) under different camera settings and three weather scenarios. The tracking results are summarized regarding Recall and precision, IDF1, IDP, IDR, MOTA, MOTP, tracking consistency, and processing time. In this study, we utilize novel Transformer architecture, a deep learning network based on the attention mechanism. Due to its strong capabilities, attention architecture has recently attracted a huge amount of attention in computer vision. This analysis showed that DETR was outperforming other algorithms in terms of tracking consistency, and it was performing similarly to FRCNN in Recall with better performance at the camera installed on intersections. Both FRCNN and DETR were outperforming YOLO5 in terms of Recall. YOLO5 is almost outperforming other algorithms in terms of Precision, MOTA, IDF1, and inference time. Considering intersections and highways, this paper has practical significance in intelligent traffic management and control.
Smart Way of Prescribing Medication

This research deals with Implementation of digital signature technology for e-prescription to avoid cybercrime problems such as Theft, modification, unauthorized access, etc. In this research, 2048-bit RSA algorithm will be implemented in Java programming and Android based system. The design of the Secure Electronic Prescription (SEP) application is to combine given services such as confidentiality, authentication and non-repudiation. The Secure Electronic Prescription (SEP)application is intended to combine specific services such as confidentiality, authentication and non-repudiation. prescription details issued to pharmacists only are used to secure encrypted prescriptions and QR codes. There are two schemes in the application namely protection schemes and authentication scheme. This study uses black-box and white-box testing to test input values, code, and outputs without testing the processes and designs occurring in the system. Demonstrate implementation of encryption in Secure Electronic Prescription (SEP). The implementation of a digital signatures in this study can prevent archive theft, which is shown in the implementation and proven in the test.
Research on Bitcoin address classification based on transaction history features

As the most popular cryptocurrency now, Bitcoin's transaction data is easy to obtain, so de-anonymizing Bitcoin becomes possible. This paper constructs a data set of Bitcoin addresses including 5 categories, analyzes and extracts the transaction features of Bitcoin addresses in more detail based on related work, and proposes two new features of fourth-order transaction moments and sample distribution. New features improve the performance of Bitcoin address classification. The accuracy of the LightGBM model was 0.94 and the F1 score was 0.91. This method can identify unknown types of Bitcoin addresses, which improves the ability of relevant agencies to investigate Bitcoin illegal activities.
Fusion and Allocation Network for Light Field Image Super-Resolution

Light field (LF) images taken by plenoptic cameras can record spatial and angular information from real-world scenes, and it is beneficial to fully integrate these two pieces of information to improve image super-resolution (SR). However, most of the existing approaches to LF image SR cannot fully fuse the information at the spatial and angular levels. Moreover, the performance of SR is hindered by the ability to incorporate distinctive information from different views and extract informative features from each view. To solve these core issues, we propose a fusion and allocation network (LF-FANet) for LF image SR. Specifically, we have designed an angular fusion operator (AFO) to fuse distinctive features among different views, and a spatial fusion operator (SFO) to extract deep representation features for each view. Following these two operators, we further propose a fusion and allocation strategy to incorporate and propagate the fusion features. In the fusion stage, the interaction information fusion block (IIFB) can fully supplement distinctive and informative features among all views. For the allocation stage, the fusion output features are allocated to the next AFO and SFO for further distilling the valid information. Experimental results on both synthetic and real-world datasets demonstrate that our method has achieved the same performance as state-of-the-art methods. Moreover, our method can preserve the parallax structure of LF and generate faithful details of LF images.
A Novel EM-Type Algorithm to Estimate Semi-Parametric Mixtures of Partially Linear Models

Semi- and non-parametric mixture of normal regression models are a flexible class of mixture of regression models. These models assume that the component mixing proportions, regression functions and/or variances are non-parametric functions of the covariates. Among this class of models, the semi-parametric mixture of partially linear models (SPMPLMs) combine the desirable interpretability of a parametric model and the flexibility of a non-parametric model. However, local-likelihood estimation of the non-parametric term poses a computational challenge. Traditional EM optimisation of the local-likelihood functions is not appropriate due to the label-switching problem. Separately applying the EM algorithm on each local-likelihood function will likely result in non-smooth function estimates. This is because the local responsibilities calculated at the E-step of each local EM are not guaranteed to be aligned. To prevent this, the EM algorithm must be modified so that the same (global) responsibilities are used at each local M-step. In this paper, we propose a one-step backfitting EM-type algorithm to estimate the SPMPLMs and effectively address the label-switching problem. The proposed algorithm estimates the non-parametric term using each set of local responsibilities in turn and then incorporates a smoothing step to obtain the smoothest estimate. In addition, to reduce the computational burden imposed by the use of the partial-residuals estimator of the parametric term, we propose a plug-in estimator. The performance and practical usefulness of the proposed methods was tested using a simulated dataset and two real datasets, respectively. Our finite sample analysis revealed that the proposed methods are effective at solving the label-switching problem and producing reasonable and interpretable results in a reasonable amount of time.