Proposed Sampling Method Research Articles

The Effects of Capital Intensity, Financial Distress, Leverage, Analyst Coverage and Investment Opportunity Set on Accounting Conservatism in Politically Connected Companies Listed on Indonesia Stock Exchange

This study aims to examine the effect of capital intensity, financial distress, leverage, analyst coverage and investment opportunity set on accounting conservatism in politically connected companies listed on the Indonesia Stock Exchange. This research was conducted on 16 state-owned companies (BUMN) listed on the Indonesia Stock Exchange for the 2017-2021 period using a proposive sampling method. This research uses multiple regression method. The results of the study show that partially analyst coverage has a positive effect on accounting conservatism. Financial distress, leverage and investment opportunity sets have a negative effect on accounting conservatism. Capital intensity has no effect on accounting conservatism.

Robot joint space grid error compensation based on three-dimensional discrete point space circular fitting

The poor absolute positioning accuracy of industrial robots has limited their application in fields such as aerospace manufacturing. To address this issue, the spatial grid compensation method has been proposed as an effective solution. In this paper, we propose a sampling method based on three-dimensional discrete point space circular fitting for grid points to significantly reduce the sampling workload and improve compensation accuracy compared to traditional joint space grid compensation methods. Additionally, we use the Kriging interpolation algorithm instead of the inverse distance weighting (IDW) algorithm for spatial interpolation prediction of pose error. Based on this, the proposed sampling and interpolation prediction method in this paper was verified on a Comau NJ500–2.7 manipulator equipped with a fiber-laying end effector. The experimental results demonstrate that using our proposed sampling method yields pose data of grid points that have only a small deviation from directly sampled results and are only slightly higher than the robot's repeat positioning accuracy. Moreover, our proposed method can significantly reduce the sampling workload by 60% under the experimental conditions of this study (sampling 10 groups of data within 90 degrees range), with increasing sampling range leading to more obvious efficiency improvements. Finally, we show that compared to the IDW algorithm, the Kriging interpolation algorithm yields better results and improves the mean absolute positioning accuracy of robots after compensation by 30%.

TCAS-PINN: Physics-informed neural networks with a novel temporal causality-based adaptive sampling method

Physics-informed neural networks (PINNs) have become an attractive machine learning framework for obtaining solutions to partial differential equations (PDEs). PINNs embed initial, boundary, and PDE constraints into the loss function. The performance of PINNs is generally affected by both training and sampling. Specifically, training methods focus on how to overcome the training difficulties caused by the special PDE residual loss of PINNs, and sampling methods are concerned with the location and distribution of the sampling points upon which evaluations of PDE residual loss are accomplished. However, a common problem among these original PINNs is that they omit special temporal information utilization during the training or sampling stages when dealing with an important PDE category, namely, time-dependent PDEs, where temporal information plays a key role in the algorithms used. There is one method, called Causal PINN, that considers temporal causality at the training level but not special temporal utilization at the sampling level. Incorporating temporal knowledge into sampling remains to be studied. To fill this gap, we propose a novel temporal causality-based adaptive sampling method that dynamically determines the sampling ratio according to both PDE residual and temporal causality. By designing a sampling ratio determined by both residual loss and temporal causality to control the number and location of sampled points in each temporal sub-domain, we provide a practical solution by incorporating temporal information into sampling. Numerical experiments of several nonlinear time-dependent PDEs, including the Cahn–Hilliard, Korteweg–de Vries, Allen–Cahn and wave equations, show that our proposed sampling method can improve the performance. We demonstrate that using such a relatively simple sampling method can improve prediction performance by up to two orders of magnitude compared with the results from other methods, especially when points are limited.

DUEN: Dynamic ensemble handling class imbalance in network intrusion detection

Network intrusion detection is an important technology for maintaining cybersecurity. The inherent difficulties co-existing in network traffic datasets, such as class imbalance, class overlapping, and noises, limit detection accuracy. However, existing research has only focused on the disproportion between classes. Considering these difficulties, a novel ensemble method is proposed for network intrusion detection called DUEN. Dynamic undersampling is incorporated into the Boosting framework to provide a relatively balanced training subset for each iteration to achieve a strong ensemble. In dynamic undersampling, the sampling number is determined based on data distribution learning to cope with the small sample overfitting problem caused by simple undersampling. The concept of classification hardness for integrating data difficulties is employed to identify samples as easy, borderline, and noisy types. Boundary samples importance enhancement mechanism is proposed for the undersampling process, preventing overfitting noise samples. The proposed sampling method does not involve distance calculation, achieving very low computational costs. Experiments show the effectiveness of DUEN with multiclass classification on two widely used datasets for evaluating intrusion detection. DUEN has high detection accuracy for both minority and majority class samples and a significant time efficiency advantage. The Macro.F1 of NSL-KDD and UNSW-NB15 datasets reached 70.7% and 50.1%, achieving 6.32% and 1.42% improvement compared with the suboptimal network intrusion detection methods. Moreover, the experimental analysis shows that DUEN has strong generalization and robustness to noise samples.

Pengaruh Struktur Modal, Tingkat Pertumbuhan, Ukuran Perusahaan terhadap Kebijakan Dividen pada Perusahaan Sektor Industri Barang Konsumsi yang Terdaftar di BEI Tahun 2018-2021

The dividend policy is a company's financial decision whether the profits earned will be distributed to shareholders or retained as retained earnings to finance future investments. Dividend policy often creates conflict between company management and shareholders. This study aims to examine the effect of capital structure, growth rate, company size on dividend policy in consumer goods industry sector companies listed on the IDX in 2018-2021. Which will be used as samples in this study, namely as many as 18 companies. In this study, the capital structure is proxied using the Debt to equity ratio (DER), the growth rate is proxied by total asset growth (Growth), and company size is proxied by log of total assets (Size). Proposive sampling method is the method used as a determinant of this research sample. The analytical method used is descriptive test, classical assumption test and multiple linear regression analysis using the SPSS application. The results of this study indicate that capital structure has a negative effect on dividend policy, company growth and company size have a positive effect on dividend policy.
 Keywords: Capital Structure, Growth Rate, Company Size, Dividend Policy

Analisis Regresi dan Korelasi Antara Kualitas Produk terhadap Keputusan Pembelian Produk Smartphone Oppo di Kota Pariaman

This study is entitled regression analysis and correlation between product quality and purchasing decisions for Oppo smartphone products in Kota Pariaman. Statistical science can be used in estimating consumer purchasing decisions involving product quality. The number of respondents amounted to 16 data taken by the proposive sampling method. The method used is Simple Linear Regression. The result of the equation is Y ̂=184.41-0.6X. The degree of relationship or Pearson correlation between variables X and Y is 77.7% which is categorized as having a high relationship. While the results of the F test obtained F count of 47.85 and F table of 4.6, because the results of F count were greater than F tables, which meant that the product quality variable had an effect on the purchasing decision variable.

Differences In Nutrient Content In Peat Land And Permanent Production Forest Area In Sei Paham Village, Sei Kepayang District, Asahan District

Peat soils are classified as marginal soils and are prone to disturbance, so efforts to increase land productivity must be followed by efforts to prevent ecosystem damage. This study aims to analyze differences in nutrient content in peat land and permanent production forest areas in Sei Paham Village, Sei Kepayang District, Asahan District . Taking 5 soil sample points was carried out using a proposive random sampling method . The soil sample is then placed in a sterile plastic bag and immediately taken to the laboratory for analysis. The parameters of the soil analysis carried out included pH , C-total, N, P, K, Ca, Na, Mg, CEC and C/N. The results showed that in permanent production forest, the nutrient content was higher than peatland in all parameters of soil chemical analysis.

Analisis Kesuburan Tanah di Pertambangan Minyak Tradisional Desa Wonocolo Kecamatan Kedewan, Kabupaten Bojonegoro

Wonocolo Village is known as a traditional mining area. This study aims to determine the level of soil contamination in traditional oil mining activities by testing N, P, K, organic matter and lime content of the soil. The method used in this research is using a proposive random sampling method. The results of testing the quality of soil in mining activities were found not to contain N,P,K. This can be seen when testing using N,P,K solutions, there was no change in soil color, organic matter and soil lime content when testing on 7 soil samples. only 2 soil samples contained organic matter and soil lime content, namely the 3rd and 7th samples. This can be seen from the presence of foam on the soil samples when dripped with H2O2 and HCL solutions. So it can be concluded that the soil in the Wonocolo mining area is highly polluted, so it is necessary to carry out bioremediation and phytoremediation the quality of soil in mining activities were found not to contain N,P,K. This can be seen when testing using N,P,K solutions, there was no change in soil color, organic matter and soil lime content when testing on 7 soil samples. only 2 soil samples contained organic matter and soil lime content, namely the 3rd and 7th samples. This can be seen from the presence of foam on the soil samples when dripped with H2O2 and HCL solutions. So it can be concluded that the soil in the Wonocolo mining area is highly polluted, so it is necessary to carry out bioremediation and phytoremediation.

Video Edukasi Kontrasepsi Islami Dalam Meningkatkan Pengetahuan PUS Tentang Metode Kontrasepsi Islami Di PMB Nenden Kopo Bandung

Information on Islamic contraception in ripe-age couples is still extremely low. The number of couples of childbearing age who are unaware of the Islamic Family Planning (KB) method is influenced by a number of factors, including education. One technique that can give information to fruitful age couples is utilizing the Varying media Strategy Wellbeing. This study was conducted at the Midwifery Clinic "Bidan Nenden" to ascertain the impact of audiovisual health education on knowledge of Islamic contraception among couples of reproductive age. The examination strategy utilizes Trial One Gathering Pretest-Posttest Plan Without Control. The proposed sampling method was used to select 45 participants for the study's sample. The findings demonstrated that prior to receiving the Audiovisual Method Health Education, respondents with less knowledge had the highest level of knowledge—21 (46.7%)—while those who received it had the highest level of knowledge—23 (51.1%). Based on the results of the paired t-test, P value of (0.000) (0.05) was found to be the effect of audiovisual health education on knowledge of Islamic contraception among couples of reproductive age, Ho is rejected. Conclusion: there is an impact of well-being instruction about contraception as indicated by Islam on the information on Discharge about contraception as per Islam. The idea is the decision of the Birthing assistance Facility 'Bidan Nenden' and well-being authorities can further develop medical care administrations in aiding Rich Age Couples by giving great Varying media Strategy Wellbeing Training.

Parallel EM optimization using improved pole‐residue‐based neuro‐TF surrogate and isomorphic orthogonal DOE sampling for microwave components design

AbstractDirect electromagnetic (EM) optimization for microwave components design is usually a time‐consuming process. To improve the optimization efficiency, this paper proposes a novel parallel EM optimization technique exploiting improved pole‐residue‐based neuro‐transfer function (neuro‐TF) surrogate and isomorphic orthogonal design of experiment (DOE) sampling strategy. We propose a new sampling method combining isomorphic orthogonal DOE and parallel EM simulations to generate training data for developing the neuro‐TF surrogate. This proposed sampling method can ensure the scattered distribution of data samples in the overall optimization process, thus effectively improving the surrogate accuracy and increasing the optimization speed. We also propose a new pole‐residue tracking technique for order‐changing to solve the discontinuity problem of pole/residues during the neuro‐TF surrogate development. Different from the fixed split ratio in existing pole‐residue tracking technique, the split ratio of poles and residues in the proposed technique is adaptive and determined according to the information of neighboring samples. Therefore, the continuity and smoothness of pole/residues after the splitting are improved, so as the neuro‐TF surrogate accuracy. In addition, the trust region algorithm is exploited during EM optimization to improve the convergence speed. In this way, the proposed EM optimization technique obtains the optimal solution in a shorter time with fewer iterations than the existing techniques. Two examples of EM optimizations of microwave components are used to illustrate the proposed technique.

TuckerDNCaching: high-quality negative sampling with tucker decomposition

Knowledge Graph Embedding (KGE) translates entities and relations of knowledge graphs (KGs) into a low-dimensional vector space, enabling an efficient way of predicting missing facts. Generally, KGE models are trained with positive and negative examples, discriminating positives against negatives. Nevertheless, KGs contain only positive facts; KGE training requires generating negatives from non-observed ones in KGs, referred to as negative sampling. Since KGE models are sensitive to inputs, negative sampling becomes crucial, and the quality of the negatives becomes critical in KGE training. Generative adversarial networks (GAN) and self-adversarial methods have recently been utilized in negative sampling to address the vanishing gradients observed with early negative sampling methods. However, they introduce the problem of false negatives with high probability. In this paper, we extend the idea of reducing false negatives by adopting a Tucker decomposition representation, i.e., TuckerDNCaching, to enhance the semantic soundness of latent relations among entities by introducing a relation feature space. TuckerDNCaching ensures the quality of generated negative samples, and the experimental results reflect that our proposed negative sampling method outperforms the existing state-of-the-art negative sampling methods.

On the Use of Circadian Cycles to Monitor Individual Young Plants

Occlusion is a very common problem in computer vision. The presence of objects seen as overlapped under a camera negatively impacts object recognition, object counting or shape estimation. This problem is especially important in plant imaging because plants are very self-similar objects which produce a lot of self-occlusions. A possible way to disentangle apparent occlusions is to acquire the same scene from different points of view when the object is motionless. Such a realization is not necessary if the objects move themselves under the camera and thus offer different points of view for free. This is the case in plant imagery, since plants have their own natural movements, including the so-called circadian rhythms. We propose to use these movements to solve some self-occlusion problems with a set of simple yet innovative sampling algorithms to monitor the growth of individualized young plants. The proposed sampling methods make it possible to monitor the growth of the individual plants until their overlap is definitive. The gain is significant with an average maximum duration of observation increase from 3 days to more than 10 days by comparison with a sampling method that would stop when the first overlap occurs.

A new sampling indicator function for stable imaging of periodic scattering media

This paper is concerned with the inverse problem of determining the shape of penetrable periodic scatterers from scattered field data. We propose a sampling method with a novel indicator function for solving this inverse problem. This indicator function is very simple to implement and robust against noise in the data. The resolution and stability analysis of the indicator function is analyzed. Our numerical study shows that the proposed sampling method is more stable than the factorization method and more efficient than the direct or orthogonality sampling method in reconstructing periodic scatterers.

Integrating satellite and street-level images for local climate zone mapping

Timely and accurate local climate zone (LCZ) classification maps are valuable for urban climate studies. The integration of remote sensing and street-level images is promising to produce high-quality LCZ maps, since the former can efficiently capture the information of landscapes on a large-scale while the latter include ground-level details. However, due to their significant differences in spatial distributions and capture views, as well as existing sampling issues of street-level images, how to fuse them effectively is challenging and remains an uncharted research area. To address these issues and fill the gap, this study proposes an effective method to integrate satellite and street-level images for LCZ mapping. Additionally, a simple yet effective street-level image sampling method is proposed. Extensive experiments have been performed and the results demonstrate the effectiveness of the proposed data fusion method and also confirm the usefulness of fusing street-level images with satellite images in enhancing the performance of LCZ mapping. Moreover, the proposed sampling method can increase data representativeness and avoid data redundancy, thus significantly reducing the number of required images while retaining high classification accuracy. To the best of our knowledge, this study is the first attempt to integrate cross-view satellite and street-level images for LCZ mapping. The study and proposed methods can contribute to the development of multi-source data fusion for LCZ map production and further benefit urban climatic research.

Identification of acoustic point sources in a two-layered medium from multi-frequency sparse far field patterns

We consider the reconstruction of point sources in a two layered medium from the multi-frequency sparse far field patterns taken on the upper half sphere. The point sources are located in both the upper half space and the lower half space, and consequently bring difficulty for the inverse problem because of the combination of two different types of far field patterns. After establishing the uniqueness of the point sources by the multi-frequency far field patterns at properly chosen sparse observation directions, we introduce a multi-step numerical scheme for identifying all the points sources. Numerical examples show that the proposed sampling methods work very well for locating the positions and the formulas for determining the corresponding scattering strengths are valid and stable with respect to the noises.

Stock Price Bubble, Debt Maturity Structur, Institusional Ownership dan Stock Price Crash Risk di Perusahaan Teknologi Indonesia

This study aims to impirically determine the influence of the stock price bubble, long-term debt and institutional ownership on the stock price crash risk in Indonesian technology companies in 2019-2021. The sampling technique in this study was using the proposive sampling method and obtained samples of 16 companies with a total observation of 48. The data analysis used in this study used the panel data regression method using the eviews 10 analysis tool. The results of this study show that long-term debt has a significant effect on stock price crash risk, meaning that companies that have a larger long-term debt structure in their capital structure have a higher stock price crash risk. Meanwhile, the stock price bubble and institutional ownership do not have a significant effect on the stock price crash risk. Simultaneously or together the stock price bubble, long-term debt and institutional ownership have a significant effect on stock price crash risk. Based on the results of the coefficient determination test, an adjusted R-square value of 0.8497 was obtained, meaning that the contribution of the independent variable in explaining the dependent stock price crash risk variable was 84.97%. This research is expected to be used as a consideration for interested parties, especially company managers and investors, to be able to pay attention to the factors that will cause stock price crash risk in Indonesian technology companies.

Sampling of graph signals with successive aggregations based on graph fractional Fourier transform

The sampling defined on the graph nodes is a crucial method for graph signals sampling, especially considering graph structure. The sampling theory of the graph frequency domain for bandlimited graph signals has blossomed in recent years. However, it fails for the graph fractional domain bandlimited signals. In this paper, we first develop the theory of successive aggregations sampling associated with the graph fractional Fourier transform (GFRFT). Then, we propose the optimal node selection scheme in the case of noise. Moreover, we present a general sampling framework and prove that the existing graph signal sampling methods are its special cases. Finally, we explore the sparse reconstruction issue based on the developed successive aggregations sampling. Our proposed sampling method outperforms other sampling schemes in reconstruction error. Several experiments are performed to validate the effectiveness of the proposed sampling method numerically.

Anharmonic thermo-elasticity of tungsten from accelerated Bayesian adaptive biasing force calculations with data-driven force fields

The elastic properties of tungsten, a ubiquitous material in future energy systems, are investigated up to its melting temperature by means of a data-driven approach. The proposed workflow combines machine learning of the force field and enhanced sampling of the crystalline structure. While the machine learning force field achieves the accuracy of ab initio calculations, its implementation in sampling methods is often limited due to its high computational cost, which is commonly a few orders of magnitude larger than that of traditional potentials. To overcome this limitation, we propose a fast and robust Bayesian sampling scheme aiming at estimating the fully anharmonic free energy of crystalline solids with the help of an improved adaptive biasing force method. This method performs a thermodynamic integration from a harmonic reference system, wherein zero frequencies associated with the periodic boundaries are screened off. The proposed sampling method drastically improves the speed of convergence and overall accuracy. We demonstrate the efficiency of the improved method by calculating the second order derivatives of the free energy, such as the elastic constants, which are performed almost 100 times faster than with the standard methods. The proposed method enables the prediction of the elastic properties of tungsten in the range of temperatures that cannot be investigated experimentally, from 2100 K to the melting temperature. The accuracy and numerical efficiency of the proposed strategy open up many avenues for the reliable prediction of finite-temperature properties of materials, such as the relative stability of structural defects and elastic constants.

Retinex-Based Relighting for Night Photography

The lighting up of buildings is one form of entertainment that makes a city more colorful, and photographers sometimes change this lighting using photo-editing applications. This paper proposes a method for automatically performing such changes that follows the Retinex theory. Retinex theory indicates that the complex scenes caught by the human visual system are affected by surrounding colors, and Retinex-based image processing uses these characteristics to generate images. Our proposed method follows this approach. First, we propose a method for extracting a relighting saliency map using Retinex with edge-preserving filtering. Second, we propose a sampling method to specify the lighting area. Finally, we composite the additional light to match the human visual perception. Experimental results show that the proposed sampling method is successful in keeping the illuminated points in bright locations and equally spaced apart. In addition, the proposed various diffusion methods can enhance nighttime skyline photographs with various expressions. Finally, we can add in a new light by considering Retinex theory to represent the perceptual color.

A geographical similarity-based sampling method of non-fire point data for spatial prediction of forest fires

Understanding the mechanisms and risks of forest fires by building a spatial prediction model is an important means of controlling forest fires. Non-fire point data are important training data for constructing a model, and their quality significantly impacts the prediction performance of the model. However, non-fire point data obtained using existing sampling methods generally suffer from low representativeness. Therefore, this study proposes a non-fire point data sampling method based on geographical similarity to improve the quality of non-fire point samples. The method is based on the idea that the less similar the geographical environment between a sample point and an already occurred fire point, the greater the confidence in being a non-fire point sample. Yunnan Province, China, with a high frequency of forest fires, was used as the study area. We compared the prediction performance of traditional sampling methods and the proposed method using three commonly used forest fire risk prediction models: logistic regression (LR), support vector machine (SVM), and random forest (RF). The results show that the modeling and prediction accuracies of the forest fire prediction models established based on the proposed sampling method are significantly improved compared with those of the traditional sampling method. Specifically, in 2010, the modeling and prediction accuracies improved by 19.1% and 32.8%, respectively, and in 2020, they improved by 13.1% and 24.3%, respectively. Therefore, we believe that collecting non-fire point samples based on the principle of geographical similarity is an effective way to improve the quality of forest fire samples, and thus enhance the prediction of forest fire risk.