bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Probabilistic-Based Portfolio Resampling Under the Mean-Variance Criterion
Authors: Wakil Anmar Al 
Published: October 2021

AbstractAn abundant amount of literature has documented the limitations of traditional unconstrained mean-variance optimization and Efficient Frontier (EF) considered as an estimation-error maximization that magnifies errors in parameter estimates. Originally introduced by Michaud (1998), empirical superiority of portfolio resampling supposedly lies in the addressing of parameter uncertainty by averaging forecasts that are based on a large number of bootstrap replications. Nevertheless, averaging over resampled portfolio weights in order to obtain the unique Resampled Efficient Frontier (REF, U.S. patent number 6,003,018) has been documented as a debated statistical procedure. Alternatively, we propose a probabilistic extension of the Michaud resampling that we introduce as the Probabilistic Resampled Efficient Frontier (PREF). The originality of this work lies in addressing the information loss in the REF by proposing a geometrical three-dimensional representation of the PREF in the mean-variance-probability space. Interestingly, this geometrical representation illustrates a confidence region around the naive EF associated to higher probabilities; in particular for simulated Global-Mean-Variance portfolios. Furthermore, the confidence region becomes wider with portfolio return, as is illustrated by the dispersion of simulated Maximum-Mean portfolios.
A Novel Grid-Based Clustering Algorithm
Authors: Starczewski Artur , Scherer Magdalena M. , Książek Wojciech , Dębski Maciej , Wang Lipo 
Published: October 2021

Abstract
Data clustering is an important method used to discover naturally occurring structures in datasets. One of the most popular approaches is the grid-based concept of clustering algorithms. This kind of method is characterized by a fast processing time and it can also discover clusters of arbitrary shapes in datasets. These properties allow these methods to be used in many different applications. Researchers have created many versions of the clustering method using the grid-based approach. However, the key issue is the right choice of the number of grid cells. This paper proposes a novel grid-based algorithm which uses a method for an automatic determining of the number of grid cells. This method is based on the kdist
function which computes the distance between each element of a dataset and its kth nearest neighbor. Experimental results have been obtained for several different datasets and they confirm a very good performance of the newly proposed method.
Decision Making Support System for Managing Advertisers By Ad Fraud Detection
Authors: Gabryel Marcin , Scherer Magdalena M. , Sułkowski Łukasz , Damaševičius Robertas 
Published: October 2021

Abstract
Efficient lead management allows substantially enhancing online channel marketing programs. In the paper, we classify website traffic into human- and bot-origin ones. We use feedforward neural networks with embedding layers. Moreover, we use one-hot encoding for categorical data. The data of mouse clicks come from seven large retail stores and the data of lead classification from three financial institutions. The data are collected by a JavaScript code embedded into HTML pages. The three proposed models achieved relatively high accuracy in detecting artificially generated traffic.
Anomaly Pattern Detection in Streaming Data Based on the Transformation to Multiple Binary-Valued Data Streams
Authors: Kim Taegong , Park Cheong Hee 
Published: October 2021

Abstract
Anomaly pattern detection in a data stream aims to detect a time point where outliers begin to occur abnormally. Recently, a method for anomaly pattern detection has been proposed based on binary classification for outliers and statistical tests in the data stream of binary labels of normal or an outlier. It showed that an anomaly pattern can be detected accurately even when outlier detection performance is relatively low. However, since the anomaly pattern detection method is based on the binary classification for outliers, most well-known outlier detection methods, with the output of real-valued outlier scores, can not be used directly. In this paper, we propose an anomaly pattern detection method in a data stream using the transformation to multiple binary-valued data streams from real-valued outlier scores. By using three outlier detection methods, Isolation Forest(IF), Autoencoder-based outlier detection, and Local outlier factor(LOF), the proposed anomaly pattern detection method is tested using artificial and real data sets. The experimental results show that anomaly pattern detection using Isolation Forest gives the best performance.
Mixup (Sample Pairing) Can Improve the Performance of Deep Segmentation Networks
Authors: Isaksson Lars J. , Summers Paul , Raimondi Sara , Gandini Sara , Bhalerao Abhir , Marvaso Giulia , Petralia Giuseppe , Pepa Matteo , Jereczek-Fossa Barbara A. 
Published: October 2021

Abstract
Researchers address the generalization problem of deep image processing networks mainly through extensive use of data augmentation techniques such as random flips, rotations, and deformations. A data augmentation technique called mixup, which constructs virtual training samples from convex combinations of inputs, was recently proposed for deep classification networks. The algorithm contributed to increased performance on classification in a variety of datasets, but so far has not been evaluated for image segmentation tasks. In this paper, we tested whether the mixup algorithm can improve the generalization performance of deep segmentation networks for medical image data. We trained a standard U-net architecture to segment the prostate in 100 T2-weighted 3D magnetic resonance images from prostate cancer patients, and compared the results with and without mixup in terms of Dice similarity coefficient and mean surface distance from a reference segmentation made by an experienced radiologist. Our results suggest that mixup offers a statistically significant boost in performance compared to non-mixup training, leading to up to 1.9% increase in Dice and a 10.9% decrease in surface distance. The mixup algorithm may thus offer an important aid for medical image segmentation applications, which are typically limited by severe data scarcity.
A New Hand-Movement-Based Authentication Method Using Feature Importance Selection with the Hotelling’s Statistic
Authors: Doroz Rafal , Wrobel Krzysztof , Porwik Piotr , Orczyk Tomasz 
Published: October 2021

Abstract
The growing amount of collected and processed data means that there is a need to control access to these resources. Very often, this type of control is carried out on the basis of bio-metric analysis. The article proposes a new user authentication method based on a spatial analysis of the movement of the finger’s position. This movement creates a sequence of data that is registered by a motion recording device. The presented approach combines spatial analysis of the position of all fingers at the time. The proposed method is able to use the specific, often different movements of fingers of each user. The experimental results confirm the effectiveness of the method in biometric applications. In this paper, we also introduce an effective method of feature selection, based on the Hotelling T2 statistic. This approach allows selecting the best distinctive features of each object from a set of all objects in the database. It is possible thanks to the appropriate preparation of the input data.
Evaluating Dropout Placements in Bayesian Regression Resnet
Authors: Shi Lei , Copot Cosmin , Vanlanduit Steve 
Published: October 2021

Abstract
Deep Neural Networks (DNNs) have shown great success in many fields. Various network architectures have been developed for different applications. Regardless of the complexities of the networks, DNNs do not provide model uncertainty. Bayesian Neural Networks (BNNs), on the other hand, is able to make probabilistic inference. Among various types of BNNs, Dropout as a Bayesian Approximation converts a Neural Network (NN) to a BNN by adding a dropout layer after each weight layer in the NN. This technique provides a simple transformation from a NN to a BNN. However, for DNNs, adding a dropout layer to each weight layer would lead to a strong regularization due to the deep architecture. Previous researches [1, 2, 3] have shown that adding a dropout layer after each weight layer in a DNN is unnecessary. However, how to place dropout layers in a ResNet for regression tasks are less explored. In this work, we perform an empirical study on how different dropout placements would affect the performance of a Bayesian DNN. We use a regression model modified from ResNet as the DNN and place the dropout layers at different places in the regression ResNet. Our experimental results show that it is not necessary to add a dropout layer after every weight layer in the Regression ResNet to let it be able to make Bayesian Inference. Placing Dropout layers between the stacked blocks i.e. Dense+Identity+Identity blocks has the best performance in Predictive Interval Coverage Probability (PICP). Placing a dropout layer after each stacked block has the best performance in Root Mean Square Error (RMSE).
Damage Detection of the Rod in the Crossflow Using Surrogate-Based Modelling
Authors: Upnere S. , Auzins J. 
Published: October 2021

Abstract
An effective and accurate methodology is developed to create an inverse surrogate model for the mass reduction analysis of the rod in the rod bundle inserted in the crossflow. The performance of two surrogate modelling approaches has been evaluated. These models are the Response Surface Method and Legendre polynomial approximations. The relationship between dominant frequencies, support stiffness and rod mass derived from Computational Fluid Dynamics simulations is used as input data for approximations. The selection of sample points is implemented with a new type of orthogonal design. The results have shown that the proposed methodology can reliably replace the finite volume model and drastically reduce computational time.
Private and Public Sector Employer Actions for Home Office Workers During the COVID-19 Epidemic
Authors: Nowacki Krzysztof , Grabowska Sandra , Lis Teresa 
Published: October 2021

Abstract
A research was conducted among the employees referred to work in the home office in connection with the announced state of the epidemic in Poland. The research was conducted using a questionnaire method, the study was attended by 199 respondents. The research is burdened with an error in the selection of statistical sample units, which resulted from the respondents’ involvement and their truthfulness. The aim of the article is to assess the activities of employers from the private and state sectors in Poland, related to the delegation of employees to work in the home office, and to analyze the skills and possibilities of self-organization of work by employees. An important result of the survey was to demonstrate the differences in the behavior of public and private sector employers. The study shows that the private sector has adapted better to sudden changes than the state sector.
Regional brands produced beyond the region: An analysis of beer brands from recently closed breweries in Europe
Authors: Hasman Jiří , Hána David , Materna Kryštof 
Published: October 2021

Abstract
Global industry has been undergoing changes in the concentration of brand ownership and production, with acquisitions and closures being a major factor in this evolution. The impacts of such activities are commonly studied from an economic perspective and from the perspective of internal firm-level and deal-level factors, while the influence of external geographical factors is largely neglected. Our research focuses mainly on the importance of geographical cultural factors affecting beer brands whose production location was moved after the closure of the original breweries. The research includes a complete sample of 30 brands from recently closed breweries across Europe. Brands are divided into seven categories according to how their marketing strategy has (or has not) changed in terms of exploiting regional and national identity. The overall success of these brands is then measured in terms of the development of their share in the home countries’ markets. Differences in brands’ strategies and successes are explained through a wide range of country-level factors and the individual characteristics of the breweries. It is shown that the level of beer tradition or identity in the countries, as well as the country’s beer life-cycle position, plays a crucial role in the evolution of the studied brands.
Indigenous journalism, media innovation, and social change: A review of previous research and call for more critical approaches
Authors: Ní Bhroin Niamh , Sand Stine , Rasmussen Torkel 
Published: October 2021

Abstract
Indigenous journalism can facilitate the inclusion of Indigenous voices in the public sphere, thereby contributing to social change. Contemporary Indigenous journalism is in part facilitated by the introduction and diffusion of paradigmatic media innovations, including the Internet, mobile technology, and social media. Based on a literature review, we investigate how media innovations are understood to facilitate Indigenous journalism and find that few empirical studies directly address this question. Analyses of Indigenous journalism, reaching beyond the potential for increased access to media and for amplification of Indigenous voice, are lacking. Furthermore, little research investigates how the appropriation of new technological affordances influence the production of Indigenous journalism. Our review also indicates that while Indigenous political participation can be facilitated by media innovation, these innovations can also serve to reinforce existing power relations. We submit that more critical analytical approaches are required to investigate how media innovations might facilitate the potential of Indigenous journalism for social change.
Sliding Mode Control of Hybrid Renewable Energy System Operating in Grid Connected and Stand-Alone Mode
Authors: Benadli Ridha , Bjaoui Marwen , Khiari Brahim , Sellami Anis 
Published: October 2021

AbstractThis paper studies innovative application of sliding mode control (SMC) for a Hybrid Renewable Energy System (HRES) in grid-connected and autonomous modes of operation. The considered HRES includes a photovoltaic (PV), wind turbine (WT) based on a Permanent Magnet Synchronous Generator (PMSG). The PV generator is coupled to the common DC bus via a DC/DC converter. The latter is controlled by an MPPT algorithm based on the Adaptive Perturbation and Observation Algorithm Method (APOAM) to search the optimum working of this source. A SMC is utilized to manage the PV voltage to achieve the Maximum Power Point (MPP) by altering the obligation duty cycle. The battery interfaced by a bidirectional buck-boost DC/DC converter can be charged or discharged depending on the production situation. On the one hand, the wind turbine conversion chain is equipped with a PMSG and a rectifier controlled to regulate the operating point of the wind turbine to its optimum value. During a Stand-Alone Mode (SAM) operation, the Voltage Source Converter (VSC) was used for controlling the output voltage in terms of amplitude and frequency delivered to the AC load. However, in Grid-Connected Mode (GCM) operation, the VSC was adapted to control the electrical parameters of the grid. To better appreciate the advantages of the proposed SMC approach, we have proposed a series of comparative tests with the conventional PI control in the operating modes GC and SA and under different scenarios. The proposed control strategy has undeniable advantages in terms of control performance and very low total harmonic distortion THD value compared with the conventional PI control. Finally, It is concluded that the proposed approach improves the quality and provides a stable operation of the HRES.
Target motion management in breast cancer radiation therapy
Authors: Piruzan Elham , Vosoughi Naser , Mahdavi Seied Rabi , Khalafi Leila , Mahani Hojjat 
Published: October 2021

Abstract

Background
Over the last two decades, breast cancer remains the main cause of cancer deaths in women. To treat this type of cancer, radiation therapy (RT) has proved to be efficient. RT for breast cancer is, however, challenged by intrafractional motion caused by respiration. The problem is more severe for the left-sided breast cancer due to the proximity to the heart as an organ-at-risk. While particle therapy results in superior dose characteristics than conventional RT, due to the physics of particle interactions in the body, particle therapy is more sensitive to target motion.

Conclusions
This review highlights current and emerging strategies for the management of intrafractional target motion in breast cancer treatment with an emphasis on particle therapy, as a modern RT technique. There are major challenges associated with transferring real-time motion monitoring technologies from photon to particles beams. Surface imaging would be the dominant imaging modality for real-time intrafractional motion monitoring for breast cancer. The magnetic resonance imaging (MRI) guidance and ultra high dose rate (FLASH)-RT seem to be state-of-the-art approaches to deal with 4D RT for breast cancer.
Towards Green and Smart Seaports: Renewable Energy and Automation Technologies for Bulk Cargo Loading Operations
Authors: Philipp Robert , Prause Gunnar , Olaniyi Eunice O. , Lemke Florian 
Published: October 2021

Abstract
In 2018, 4.1 billion tonnes of freight and 437 million passengers passed through the 1200 European ports. This dimension of geographically concentrated activities is the rationale that ports are characterised by a high-energy demand and a high share of emissions. Driven by a growing awareness for a cleaner environment, a stronger focus on sustainability and intensified environmental regulations, ports are forced to take responsibility when it comes to environmental issues. As a response, in recent studies, the concept of ‘green ports’ emerged. Simultaneously, in the context of digitalisation, the term ‘smart ports’ has received growing attention in the latest scientific discussions, too. Since an important driver towards greener maritime operations is linked to digitalisation, we argue that digital efforts in ports should next to the automation of inherent logistics processes also contribute to reducing the emissions and energy demands. Previous studies have primarily concentrated on the automation of container handling operations. Hence, there exists a research gap concerning the automation of bulk cargo handling operations in ports. Thus, this study addresses the question of how to automate the dry bulk cargo loading operations in the frame of a green and smart port development. The developed case study refers to the seaport of Wismar, whereby the results show that the digitalisation and greener port operations can be successfully aligned. Overall, this study extends the discussion on green and smart port development, while it contributes to the scientific literature by proving that both conceptual ideas can be achieved in the operating business.
Numerical Study on Erosion Wear and Strength of Main Gas Pipelines Bends
Authors: Yaroslav Doroshenko , Galyna Kogut , Yuliia Doroshenko , Oleh Tarayevs’kyy , Taras Pyrig 
Published: October 2021

Abstract
The purpose of this work is to ensuring the strength of main gas pipelines bends by studying the peculiarities of single-phase and multiphase flows movement through the internal cavity, the processes of erosion wear and the wall stress state. The problem of synergistic influence of gas-dynamic processes (uneven pressure distribution in the internal cavity), temperature difference and erosion wear on the stress state of the bends of main gas pipelines was solved by numerical simulation. Based on the results of simulation the processes of bends erosion wear, an algorithm for three-dimensional simulation of bend walls erosion defects was developed. The complex three-dimensional geometric shape of the erosion defects of the bend wall varied according to the rate of erosion wear process. This algorithm made it possible to determine the regularities for the influence of the bend erosion defects magnitude on bends stress state. It was established that considering the maximum depth of bend erosion defects 9.6 mm, 10.5 mm and 11.9 mm, the equivalent stresses in the deepest places of the erosion defect were greater than on the concave side of the bend and in straight sections of the pipeline.
Financial Stability dan Misstatement Laporan Keuangan dengan Opportunity Fraud sebagai Variabel Moderating
Authors: Suryawan Kadek Yoga , Budiasih I Gusti Ayu Nyoman 
Published: October 2021

The research aims to obtain empirical evidence of the influence of Financial Stability on Misstatement of Financial Statements and to determine the ability of Opportunity Fraud to moderate the influence of Financial Stability on Misstatement of Financial Statements. This research was conducted at the Indonesia Stock Exchange (IDX) which has corporate financial data, especially manufacturing companies. The method of determining the sample using a purposive sampling technique. The number of samples in this study were 308 observational data. The analysis technique used is Moderated Regression Analysis (MRA). The results showed that Financial stability had a positive effect on the misstatement of financial statements of manufacturing companies listed on the Indonesia Stock Exchange in 2015-2018 and Opportunity Fraud weakened the influence of Financial Stability on the Misstatement of Financial Statements of manufacturing companies listed on the Indonesia Stock Exchange in 2015 - 2018.
Keywords: Experience; Competence; Independence; Audit Fees; Quality Audits.
Alokasi Belanja Rutin dan Belanja Modal pada Indeks Pembangunan Manusia Tahun Anggaran 2015-2017
Authors: Kakadir Petrus , Suaryana I Gusti Ngurah Agung , Putri I Gusti Ayu Made Asri Dwija , Wirawati Ni Gusti Putu 
Published: October 2021

This study aims to determine the effect of routine and capital expenditure allocations on the human development index in the regency/municipality of Papua province. This study covers 29 districts/cities in the province of Papua using an associative quantitative approach. The analysis technique used is multiple linear regression analysis technique. The results of the analysis show that routine expenditures have a positive and significant effect on the human development index, while capital expenditures have a negative and non-significant effect on the human development index. The failure of capital expenditures to affect the human development index occurred, in addition to the low amount of capital expenditure allocated in an effort to improve people's welfare, also due to the allocation of capital expenditures that had not been carried out on target.
Keywords: Routine Shopping; Capital Expenditures; Human Development Index.