Total Amount Of Data Research Articles

Deep Reinforcement Learning based running-track path design for fixed-wing UAV assisted mobile relaying network

This paper studies a fixed-wing unmanned aerial vehicle (UAV) assisted mobile relaying network (FUAVMRN), where a fixed-wing UAV employs an out-band full-duplex relaying fashion to serve a ground source-destination pair. It is confirmed that for a FUAVMRN, straight path is not suitable for the case that a huge amount of data need to be delivered, while circular path may lead to low throughput if the distance of ground source-destination pair is large. Thus, a running-track path (RTP) design problem is investigated for the FUAVMRN with the goal of energy minimization. By dividing an RTP into two straight and two semicircular paths, the total energy consumption of the UAV and the total amount of data transferred from the ground source to the ground destination via the UAV relay are calculated. According to the framework of Deep Reinforcement Learning and taking the UAV's roll-angle limit into consideration, the RTP design problem is formulated as a Markov Decision Process problem, giving the state and action spaces in addition to the policy and reward functions. In order for the UAV relay to obtain the control policy, Deep Deterministic Policy Gradient (DDPG) is used to solve the path design problem, leading to a DDPG based algorithm for the RTP design. Computer simulations are performed and the results show that the DDPG based algorithm always converges when the number of training iterations is around 500, and compared with the circular and straight paths, the proposed RTP design can save at least 12.13 % of energy and 65.93 % of flight time when the ground source and the ground destination are located 2000 m apart and need to transfer 5000bit/Hz of data. Moreover, it is more practical and efficient in terms of energy saving compared with the Deep Q Network based design.

An extreme thermal cycling reliability test of ATLAS ITk Strips barrel modules

At the end of Run 3 of the Large Hadron Collider (LHC), the accelerator complex will be upgraded to the High-Luminosity LHC (HL-LHC) in order to increase the total amount of data provided to its experiments. To cope with the increased rates of data, radiation, and pileup, the ATLAS detector will undergo a substantial upgrade, including a replacement of the Inner Detector with a future Inner Tracker, called the ITk. The ITk will be composed of pixel and strip sub-detectors, where the strips portion will be composed of 17,888 silicon strip detector modules. During the HL-LHC running period, the ITk will be cooled and warmed a number of times from about -35°C to room temperature as part of the operational cycle, including warm-ups during yearly shutdowns. To ensure ITk Strips modules are functional after these expected temperature changes, and to ensure modules are mechanically robust, each module must undergo ten thermal cycles and pass a set of electrical and mechanical criteria before it is placed on a local support structure. This paper describes the thermal cycling Quality Control (QC) procedure, and results from the barrel pre-production phase (about 5% of the production volume). Additionally, in order to assess the headroom of the nominal QC procedure of 10 cycles and to ensure modules don't begin failing soon after, four representative ITk Strips barrel modules were thermally cycled 100 times — this study is also described.

Efektivitas penerapan pembayaran online berbasis e-Samsat dalam penerimaan pajak kendaraan bermotor pada Badan Pendapatan Daerah Provinsi Sulawesi Utara

In the current era where everything can be done using technology, including paying vehicle tax, the E-Samsat online application is expected to increase Regional Original Income in North Sulawesi Province. Therefore, I want to know the effectiveness of implementing E-Samsat in the community environment in the Regional Revenue Agency of North Sulawesi Province. This type of research is descriptive qualitative. The results of the research show that there is an increase in the target and realization of Motor Vehicle Tax revenue in 2020-2022, but it decreases in 2023. Judging from the data on Motor Vehicle Tax revenue through the E-Samsat based online application in 2020-2022 there is also an increase, and the same Just as target and realization data will decline in 2023, revenue via E-Samsat will also experience a decline in the number of people paying vehicle tax. However, according to the effectiveness ratio criteria determined by the Ministry of Home Affairs through Minister of Home Affairs Decree No. 690,900,327 concerning Guidelines for Financial Performance Assessment, the total amount of target data and realization of Motor Vehicle Tax revenue is included in the effective category. One of the factors that has not achieved the target and realization of Motor Vehicle Tax revenue is the lack of equitable outreach to all regions. Therefore, the Government, through the Regional Revenue Agency of North Sulawesi Province, must provide more extensive outreach or outreach to the community directly.

Exploring Indonesian EFL students’ lexical diversity and its correlation with academic vocabulary use in an online academic writing environment

This paper reports on a quasi-experimental study designed to explore the relationship between lexical diversity and academic vocabulary use among Indonesian EFL university students engaging in online academic writing discussions and assignments. The study analysed 11,624 tokens and 5437 types collected from students' contributions in online discussions and academic writing assignments. Findings revealed that the level of lexical diversity among the students was significantly varied depending on the text length and the total amount of data. It was found most students were indicated to possess average ESL level of academic writing with a D value ranging from 50 to 70 while some others indicated a high level of adult ESL with developed academic text in the range between 70 and 80 D level. Moreover, it was also found that the degree of lexical diversity was not affected by the writing topic familiarity or students' academic year. Findings revealed a statistically significant negative correlation between measures of lexical diversity and academic vocabulary use. Contrary to expectations, while a diversified vocabulary generally correlates positively with student writing performance, this relationship reverses in contexts emphasizing the academic quality of writing. These results highlight the complex dynamics of lexical usage in academic writing and suggest that mere lexical diversity does not always equate to higher academic writing quality. This study contributes to a critical understanding of how lexical diversity functions in digital academic environments and offers implications for English language teaching practices in higher education settings.

Distributed Batch Learning of Growing Neural Gas for Quick and Efficient Clustering

Growing neural gas (GNG) has been widely used in topological mapping, clustering and unsupervised tasks. It starts from two random nodes and grows until it forms a topological network covering all data. The time required for growth depends on the total amount of data and the current network nodes. To accelerate growth, we introduce a novel distributed batch processing method to extract the rough distribution called Distributed Batch Learning Growing Neural Gas (DBL-GNG). First, instead of using a for loop in standard GNG, we adopt a batch learning approach to accelerate learning. To do this, we replace most of the standard equations with matrix calculations. Next, instead of starting with two random nodes, we start with multiple nodes in different distribution areas. Furthermore, we also propose to add multiple nodes to the network instead of adding them one by one. Finally, we introduce an edge cutting method to reduce unimportant links between nodes to obtain a better cluster network. We demonstrate DBL-GNG on multiple benchmark datasets. From the results, DBL-GNG performs faster than other GNG methods by at least 10 times. We also demonstrate the scalability of DBL-GNG by implementing a multi-scale batch learning process in it, named MS-DBL-GNG, which successfully obtains fast convergence results. In addition, we also demonstrate the dynamic data adaptation of DBL-GNG to 3D point cloud data. It is capable of processing and mapping topological nodes on point cloud objects in real time.

Immersive E-learning mode application in Chinese language teaching system based on big data recommendation algorithm

With the rapid development of information technology, E-Learning has become an innovative teaching method. However, in the field of Chinese teaching, how to provide effective learning resources and recommendation algorithms in E-Learning mode is still a challenge. This study aims to improve the effectiveness of Chinese teaching system and students’ learning outcomes through an immersive E-Learning model based on big data interest recommendation algorithm. This paper adopts an immersive E-Learning model based on big data interest recommendation algorithm, and constructs a Chinese teaching system. The web crawler is used to fully collect the experimental data and collate it in a targeted manner, and the required data is screened out by using a more efficient separation method. Adding big data recommendation algorithm to the system of this paper can not only record and analyze historical behaviors of users, but also recommend data information according to users’ interests, so that users can clarify their real information needs. By testing the system’s professional ability and recording the experimental data, this paper finds that the overall performance of this Chinese language teaching system is very good, and can achieve the original expected design purpose. In addition, the system largely solves the problem that the traditional system based on data recommendation algorithm is difficult to carry out effective recommendation smoothly when the total amount of data is too large.

Enhancing Power Grid Data Analysis with Fusion Algorithms for Efficient Association Rule Mining in Large-Scale Datasets

Against the backdrop of the rapid development of information technology, the total amount of data has exploded, and efficient association rule mining methods for large-scale datasets have been studied. Conventional rule mining algorithms are subject to electrical constraints when working, and their convergence speed and data noise are currently the main problems they face. In order to accelerate the working process of the algorithm, this study introduces a data warehouse into the K-Means algorithm, and connects the time series and voltage interaction functions with the long-and-short-term memory network for efficient information analysis of power grid data, generating fusion algorithms. The study conducted experiments on the Netloss dataset and simultaneously conducted experiments on three models, including long-and-short-term memory networks, to verify the superiority of the fusion algorithm. Under the same experimental voltage, the circuit power flows of the four models were 0.37, 0.64, 0.79, and 0.82A, respectively, indicating that the algorithm effectively controlled the electrical dataset. Its measurement accuracy was the highest among the four models, at 91.7%. The experimental results show that the fusion algorithm proposed in the study has precise control ability in power grid datasets, and can effectively mine association rules on large-scale datasets.

A restrictive design strategy of diffuse metasurfaces based on fibonacci sequences for RCS reduction

The rapid and efficient design strategy represents a highly intriguing research domain within the realm of metasurfaces. It necessitates minimizing the data load in coding metasurface design, facilitating the development of a precise approach to designing metasurfaces with specific characteristics. In this paper, we proposed a restrictive design strategy of metasurface for coding phase distributions based on the Fibonacci sequences, which is efficient to optimize designated radar cross section (RCS) reduction according to application requirements. Through the given restrictive strategy, the encoding range of the metasurfaces is reduced from the entire metasurfaces to the initial phase units in the corners, greatly reducing the total amount of data in the optimization database. In order to verify the availability of the strategy, RCS reduction metasurfaces with variable phase sequences in microwave region are analyzed. For broadband and wide-angle RCS reduction, the optimized Fibonacci discrete phase gradient (FDPG) metasurface is obtained by adjusting the phase sequences. Both the simulation and measured results show that the optimized FDPG metasurfaces by the proposed designing strategy have the RCS reduction of −10 dB in the frequency ranges of 6 GHz to 18 GHz under wide incidence angles from 0° to 30°. The simulated and measured results are good consistent over the whole frequency ranges and angle regions. This method combines mathematical sequences with physics organically, making it an important field in metasurfaces research and it is expected to play an important role in future studies.

BioVDB: biological vector database for high-throughput gene expression meta-analysis.

High-throughput sequencing has created an exponential increase in the amount of gene expression data, much of which is freely, publicly available in repositories such as NCBI's Gene Expression Omnibus (GEO). Querying this data for patterns such as similarity and distance, however, becomes increasingly challenging as the total amount of data increases. Furthermore, vectorization of the data is commonly required in Artificial Intelligence and Machine Learning (AI/ML) approaches. We present BioVDB, a vector database for storage and analysis of gene expression data, which enhances the potential for integrating biological studies with AI/ML tools. We used a previously developed approach called Automatic Label Extraction (ALE) to extract sample labels from metadata, including age, sex, and tissue/cell-line. BioVDB stores 438,562 samples from eight microarray GEO platforms. We show that it allows for efficient querying of data using similarity search, which can also be useful for identifying and inferring missing labels of samples, and for rapid similarity analysis.

What's in your PIE? Understanding the contents of personalized information environments with PIEGraph

AbstractSocial media have long been studied from platform‐centric perspectives, which entail sampling messages based on criteria such as keywords and specific accounts. In contrast, user‐centric approaches attempt to reconstruct the personalized information environments users create for themselves. Most user‐centric studies analyze what users have accessed directly through browsers (e.g., through clicks) rather than what they may have seen in their social media feeds. This study introduces a data collection system of our own design called PIEGraph that links survey data with posts collected from participants' personalized X (formerly known as Twitter) timelines. Thus, in contrast with previous research, our data include much more than what users decide to click on. We measure the total amount of data in our participants' respective feeds and conduct descriptive and inferential analyses of three other quantities of interest: political content, ideological skew, and fact quality ratings. Our results are relevant to ongoing debates about digital echo chambers, misinformation, and conspiracy theories; and our general methodological approach could be applied to social media beyond X/Twitter contingent on data availability.

A Cost-Aware Utility-Maximizing Bidding Strategy for Auction-Based Federated Learning.

Auction-based federated learning (AFL) has emerged as an efficient and fair approach to incentivize data owners (DOs) to contribute to federated model training, garnering extensive interest. However, the important problem of helping data consumers (DCs) bid for DOs in competitive AFL settings remains open. Existing work simply treats that the actual cost paid by a winning DC (i.e., the bid cost) is equal to the bid price offered by that DC itself. However, this assumption is inconsistent with the widely adopted generalized second-price (GSP) auction mechanism used in AFL, including in these existing works. Under a GSP auction, the winning DC does not pay its own proposed bid price. Instead, the bid cost for the winner is determined by the second-highest bid price among all participating DCs. To address this limitation, we propose a first-of-its-kind federated cost-aware bidding strategy () to help DCs maximize their utility under GSP auction-based federated learning (FL). It enables DCs to efficiently bid for DOs in competitive AFL markets, maximizing their utility and improving the resulting FL model accuracy. We first formulate the optimal bidding function under the GSP auction setting, and then demonstrate that it depends on utility estimation and market price modeling, which are interrelated. Based on this analysis, jointly optimizes in a novel end-to-end framework, and then executes the proposed return on investment (ROI)-based method to determine the optimal bid price for each piece of the data resource. Through extensive experiments on six commonly adopted benchmark datasets, we show that outperforms eight state-of-the-art methods, beating the best baseline by 4.39%, 4.56%, 1.33%, and 5.43% on average in terms of the total amount of data obtained, number of data samples per unit cost, total utility, and FL model accuracy, respectively.

Exploration of Cultural Teaching Mechanisms of Ideology and Politics Courses under the Background of “Internet Plus”

Abstract As the cradle of cultivating and conveying talents, colleges and universities should innovate the education of ideological and political courses under the background of “Internet+”, focusing on cultivating students’ cultural self-confidence. This paper first explains blockchain-related technology in Internet technology, including the basic principle and structure of blockchain, the consistency Hash algorithm, the consensus mechanism and the credit mechanism of blockchain. Secondly, it discusses the fit between the blockchain and the cultural teaching mechanism of the Civics course, through which blockchain technology can effectively realize the interactive sharing of the cultural teaching resources of the Civics course. Based on the blockchain technology and MVC network structure, an interactive model of cultural teaching resources for Civic and Political Science courses is constructed, which further improves the way students access cultural teaching resources. Finally, a simulation test is conducted to evaluate the interactivity of the cultural teaching resources in the Civics and Political Science course and the effectiveness of the model. The results show that the average write bandwidth of the consistent Hash algorithm is 756.68MBps, and the average read bandwidth is 215.42MBps, and when the total amount of data exceeds 64GB, the relative standard deviation of the Hash algorithm decreases to about 0.15% and remains stable. The cultural teaching mechanism of the Civics course with the introduction of blockchain technology is based on a digital platform, and when the number of network nodes is 150, the delay of the consensus mechanism is 236.73ms, and the throughput reaches about 98 data interactions per second. The cultural teaching mechanism of the Civics and Politics course in the background of the Internet needs to be based on digitization, fully combining with information technology to realize the common construction and sharing of cultural teaching resources and cultivate new-age talents with higher cultural self-confidence for the development of society.

Max–Min Fair 3D Trajectory Design and Transmission Scheduling for Solar-Powered Fixed-Wing UAV-Assisted Data Collection

This paper presents a new three-dimensional (3D) trajectory design approach for a solar-powered, fixed-wing unmanned aerial vehicle (UAV) to harvest solar energy and collect data from multiple smart devices (SDs). The trajectory is optimized based on max-min fairness to balance the total amount of uploaded data and the fairness among the SDs. The key idea is that we develop non-trivial variable substitution and successive convex approximation (SCA) techniques to convexify data transmission, UAV energy consumption and mobility, and energy harvesting constraints under a persistent round-robin transmission schedule of the SDs. The resulting algorithm guarantees a locally optimal trajectory satisfying the Karush-Kuhn-Tucker (KKT) conditions. Another important aspect is that we further jointly optimize the transmission schedule along with the trajectory, and prove that absolute fairness in terms of uploaded data can be achieved among the SDs under the max-min fairness. The new algorithms apply to both line-of-sight (LoS)-dominant and probabilistic SD-UAV channels. Numerical results show that a 3D trajectory increases the uploaded data by 111%, compared to a two-dimensional (2D) trajectory. The proposed algorithms can balance the energy harvesting and data collection, and achieve fairness in both LoS-dominant and probabilistic SD-UAV channels.

PENGARUH CAPITAL INTENSITY, INVENTORY INTENSITY, INSTITUTIONAL OWNERSHIP SEBAGAI VARIABEL MODERASI TERHADAP TAX AVOIDANCE

This study aims to determine the influence of capital intensity, inventory intensity, institutional ownership as moderation variables on tax avoidance in energy companies listed on the IDX in 2017-2021. This type of research is associative quantitative, where research methods based on the philosophy of positivism, are used to examine in certain populations or samples, which are in the nature of asking the influence of two or more variables on other variables. The type of data used is secondary data, namely financial statement data obtained from the IDX. The sampling method in this study used purposive sampling techniques with certain criteria. The total amount of data obtained in this study was 45 financial statements from 9 companies. The testing in this study was assisted by the Eviews12 software, and the model used was the regression of the Fixed effect Model (FEM) approach panel data. The results of this study show that simultaneously capital intensity, inventory intensity together affect tax avoidance. Meanwhile, partial capital intensity does not affect tax avoidance, while inventory intensity has a positive effect on tax avoidance, institutional ownership moderation is unable to strengthen the capital intensity relationship to tax avoidance, and institutional ownership moderation is unable to strengthen inventory intensity relationship to tax avoidance

Analisis Fundamental Terhadap Harga Saham Pada Saat Pandemi

The COVID-19 pandemic caused the BEI sectoral index to experience declines and increases in share prices during the 2020 period. This research aims to provide fundamental analysis of share prices. Case Study of the 2020-2021 LQ45 Company Listed on the Indonesian Stock Exchange. The population in this research was 45 companies, and based on the sample selection criteria above, the research sample was 19 companies and the total amount of data examined was 38 data. The results of this research are to partially explain the variables Debt To Equity Ratio, Price To Book Value, Return On Equity, Price Earning Ratio, And Earnings per share which simultaneously influence Stock Prices. Meanwhile, partially the Debt To Equity Ratio and Price Earning Ratio have no effect on Stock Prices, while price to book value, return on equity, and Earnings Per Share partially have an effect on stock prices.

Pengaruh Kinerja Keuangan, Ukuran dan Umur Perusahaan Terhadap Nilai Perusahaan dengan Pengungkapan Corporate Social Responsibility (CSR) Sebagai Variabel Moderasi

This research aims to examine the influence of financial performance, company size, and age on company value with corporate social responsibility (CSR) disclosure. The dependent variable in this research is Company Value with Corporate Social Responsibility (CSR) Disclosure. Meanwhile, the independent variables are Financial Performance, Company Size, and Age. The population in this study were manufacturing companies listed on the Indonesian Stock Exchange for the 2016 - 2020 period. The sampling method used was purposive so 7 samples were obtained. The total amount of data processed in this research was 35 data. The type of data used is secondary data. The data analysis method used in this research is multiple regression. The results of this research indicate that financial performance has a significant effect on company value. Meanwhile, company size and age do not significantly affect company value.

PENGARUH PENGELUARAN PEMERINTAH SEKTOR KESEHATAN DAN PENDIDIKAN TERHADAP INDEKS PEMBANGUNAN MANUSIA (IPM)

This study aims to test and analyze the effect of government expenditure on the health and education sectors on HDI. This research uses a quantitative approach. The population of this research was provincial governments in Indonesia in 2017-2021, and purposive sampling was used to select the research sample. The research sample amounted to 31 with a period of 5 years, which means the total amount of data is 155. The data analysis technique uses panel data regression analysis assisted by the Eviews application. The findings of this study are that government expenditure on the health sector does not affect HDI. In contrast, government expenditure on education affects HDI and its components, namely life expectancy, education, and per capita expenditure. This study implies that the management of health and education funds needs to be optimized to improve human development. The government needs to think carefully about how to use these funds effectively and on target. A well-targeted budget allocation will support the achievement of increased human development.

Pengaruh Fee Audit, Audit Tenure, dan Spesialisasi Auditor Terhadap Kualitas Audit: Studi Empiris pada Perusahaan Manufaktur di BEI 2017-2021

This study aims to prove the effect of audit fees, audit tenure and educational specifications on audit quality in manufacturing companies in the Indonesian capital market. The sample size used was 44 companies. The data used is from 2017 to 2021, so the total amount of data processed is 220 observations. The data is obtained from the annual report published by the Indonesia Stock Exchange. The analytical method used is multiple regression while testing the hypothesis is done by statistical t-test. In accordance with the results of hypothesis testing, it was found that fee audits and education specifications individually have a positive and significant effect on audit quality in manufacturing companies on the Indonesia Stock Exchange, while tenure audits have no significant effect on audit quality in manufacturing companies on the Indonesia Stock Exchange from 2017 to 2021.

Analisis Sentimen E-Wallet Menggunakan Support Vector Machine Berbasis Particle Swarm Optimization

E-Wallet applications in Indonesia have started to be in demand since the Covid-19 pandemic. The object being analyzed is an e-wallet application that is widely used in Indonesia and can be downloaded on the Google Playstore. The applications analyzed are Dana, Ovo, PayPal Link Aja and Doku. The advantages of these five applications are that Dana is user friendly or easy to use, while using Ovo is superior in terms of benefits, and Doku is superior in terms of security, Link Aja tends to be perceived by consumers in a neutral condition between security and user convenience because it is an e-wallet. It is still considered new in Indonesia, and PayPal has become a successful online payment system in C2C field. The focus of this research is to compare the comments of the users of the five applications. The method used in this study is the Support Vector Machine (SVM) algorithm. To produce high accuracy it is optimized using the Particle Swarm Optimization (PSO) algorithm. This was taken based on previous studies which stated that SVM-PSO has the highest percentage of accuracy compared to other algorithms. The data used is a thousand (1000) per application. So, the total amount of data is five thousand (5000) data. The results of the research show that the Ovo e-wallet is superior because it has the most positive comments, namely 579 and 421 negative comments, while the lowest position is occupied by Link Aja which only has 579 positive comments and 421 negatuve comments. In the process of sentiment analysis, the accuracy percentage of the SVM-PSO algorithm was also obtained, which was 91.10% in the Link Aja application. This means that SVM-PSO is very suitable to be combined to get the highest accuracy

Detection of the gene mutation of epidermal growth factor receptor in lung adenocarcinoma by radiomic features from a small amount of PET data.

The purpose of this work was to identify the potential mutation of epidermal growth factor receptor in nonsmall cell adenocarcinoma by noninvasive method, and to explore whether the same or better effect can be achieved using a small amount of single-mode PET image data. A total of 115 patients were recruited and the results of their 18F-FDG PET images and gene detection after resection were obtained; 117 original radiation features and 744 wavelet transform features were extracted from PET images. Several methods were used to reduce the dimension of the data, and four classifier models were established to classify it. The above process was repeated to reduce the total amount of data and the area under the receiver operating characteristic curve (AUC) value that changed with the reduction of the data and the stability of the results was recorded. The classifier with the best comprehensive performance under this dataset was logistic regression, whose AUC value is 0.843. And similar results can be obtained from only 30 cases of data. A similar or better result could be achieved using a small number of single-mode PET images. In addition, significant results could be obtained using only the PET images of 30 patients.